def begin_collision_detection(self,
dynamic_detection=False,
callback=None,
static_collision_type=None,
debug=False):
if not self.perception_started:
err("Perception hasn't been started!")
return False
# if not self.detect_ready:
# err("ready_collision_detection hasn't been called")
# return False
self.is_dynamic = dynamic_detection
self.callback = callback
if not dynamic_detection:
self._setup_collision_type(static_collision_type)
self.collision_detected = False
self.collision_finished = False
self.debug = debug
self.collision_type = "No collision"
self.cur_iter = 0
self.cur_inds = {}
for k in self.perceptions:
self.cur_inds[k] = 0
self.i_data_buffers = {}
self.t_data_buffers = {}
for k in self.perceptions:
self.i_data_buffers[k] = [
np.zeros(
(self.impact_fgc.filter_len, self.impact_fgc.filter_len))
for i in range(self.perception_lengths[k])
]
self.t_data_buffers[k] = [
np.zeros((self.type_fgc.filter_len, self.type_fgc.filter_len))
for i in range(self.perception_lengths[k])
]
self.end_monitor_lock = threading.Lock()
self.data_thread_spawner_lock1 = threading.Lock()
self.data_thread_spawner_lock2 = threading.Lock()
self.data_thread_spawner = RateCaller(self._data_spawn_thread,
self.SAMPLING_RATE)
self.data_thread_spawner.run()
self.beg_time = rospy.Time.now().to_sec()
self.t_sum, self.t_num = 0., 0.
self.normal_dict_counts = []
self.temp_dict = {}
for k in self.perceptions:
self.temp_dict[k] = [[] for i in range(self.perception_lengths[k])]
return True
def get_zeros(self, time=4.):
self._zeros = {}
for k in self.perceptions:
self._zeros[k] = None
self._n = 0
monitor = RateCaller(self._sum_values, self.rate)
monitor.run()
rospy.sleep(time)
monitor.stop()
for k in self.perceptions:
self._zeros[k] /= self._n
return self._zeros
def start_data_capture(self, duration=None):
if not self.perception_started:
err("Perception hasn't been started!")
return False
self.logger = RateCaller(self._gather_perception, self.SAMPLING_RATE)
for k in self.perceptions:
self.datasets[k] += [[]]
self.logger.run()
if not duration is None:
threading.Timer(self.stop_data_capture, duration)
return True
def start_data_capture(self, duration=None):
if not self.perception_started:
err("Perception hasn't been started!")
return False
self.logger = RateCaller(self._gather_perception, self.SAMPLING_RATE)
for k in self.perceptions:
self.datasets[k] += [[]]
self.logger.run()
if not duration is None:
threading.Timer(self.stop_data_capture, duration)
return True
def begin_monitoring(self, models=None, model_zeros=None,
std_dev_dict=None, noise_dev_dict=None, duration=None,
std_dev_default=2.0, noise_dev_default=0.25,
tol_thresh_dict=None,
contingency=None, window_size=70, current_zeros=None,
sampling_rate = 1,
only_pressure=False,
transform_dict=None, verbose=True, collide=True):
if self.active:
log("Perception already active.")
return
self.active = True
self.setup_monitoring(models,
std_dev_dict=std_dev_dict, noise_dev_dict=noise_dev_dict,
duration=duration, std_dev_default=std_dev_default,
noise_dev_default=noise_dev_default,
tol_thresh_dict=tol_thresh_dict,
contingency=contingency, window_size=window_size,
sampling_rate=sampling_rate,
current_zeros=current_zeros, transform_dict=transform_dict,
verbose=verbose, collide=collide)
# if models is not None:
# self.models = models
if model_zeros is not None:
self.model_zeros = model_zeros
self.cur_pt = 0
self.collision_times = {}
self.collision_sums = {}
self.cur_col_time = 0
self.current_zeros = {}
self.z_sum = {}
self.z_rsum = {}
self.z_list = {}
self.min_prob = 100000.0
self.cur_mals = {}
self.mals = None
self.only_pressure = only_pressure
self.monitor = RateCaller(self._monitor_data, self.rate * self.sampling_rate)
self.monitor.run()
# self.sumcalls = {}
# self.sumsave = {}
if not duration is None:
self.dur_timer = threading.Timer(self.end_monitoring, duration)
self.dur_timer.start()
def begin_collision_detection(self, dynamic_detection=False,
callback=None, static_collision_type=None,
debug=False):
if not self.perception_started:
err("Perception hasn't been started!")
return False
# if not self.detect_ready:
# err("ready_collision_detection hasn't been called")
# return False
self.is_dynamic = dynamic_detection
self.callback = callback
if not dynamic_detection:
self._setup_collision_type(static_collision_type)
self.collision_detected = False
self.collision_finished = False
self.debug = debug
self.collision_type = "No collision"
self.cur_iter = 0
self.cur_inds = {}
for k in self.perceptions:
self.cur_inds[k] = 0
self.i_data_buffers = {}
self.t_data_buffers = {}
for k in self.perceptions:
self.i_data_buffers[k] = [ np.zeros((self.impact_fgc.filter_len, self.impact_fgc.filter_len)) for i in range(self.perception_lengths[k])]
self.t_data_buffers[k] = [ np.zeros((self.type_fgc.filter_len, self.type_fgc.filter_len)) for i in range(self.perception_lengths[k])]
self.end_monitor_lock = threading.Lock()
self.data_thread_spawner_lock1 = threading.Lock()
self.data_thread_spawner_lock2 = threading.Lock()
self.data_thread_spawner = RateCaller(self._data_spawn_thread, self.SAMPLING_RATE )
self.data_thread_spawner.run()
self.beg_time = rospy.Time.now().to_sec()
self.t_sum, self.t_num = 0., 0.
self.normal_dict_counts = []
self.temp_dict = {}
for k in self.perceptions:
self.temp_dict[k] = [[] for i in range(self.perception_lengths[k])]
return True
class ArmPerceptionMonitor( ):
##
# Initializes internal variables
#
# @param arm 0 if right, 1 if left
# @param percept_mon_list list of perceptions to monitor; if None, do all
def __init__(self, arm):
self.load_parameters()
############## Classifiers #################
self.impact_classifier = cfiers.classifiers_dict["small_refined_random_forest"]
self.coll_type_classifier = cfiers.classifiers_dict["large_refined_random_forest"]
log("Loading impact classifier")
self.impact_classifier.load(self.I_RTREE_CLASSIFIER)
log("Loading collision type classifier")
self.coll_type_classifier.load(self.T_RTREE_CLASSIFIER)
if arm == 0:
self.armc = "r"
self.is_right_arm = True
else:
self.armc = "l"
self.is_right_arm = False
self.perceptions = {}
self.perception_names = [ "accelerometer",
"joint_angles",
"joint_velocities",
"joint_efforts",
"r_finger_periph_pressure",
"r_finger_pad_pressure",
"l_finger_periph_pressure",
"l_finger_pad_pressure",
"gripper_pose"]
self.perception_lengths = { "accelerometer" : 3,
"joint_angles" : 7,
"joint_velocities" : 7,
"joint_efforts" : 7,
"r_finger_periph_pressure" : 1,
"r_finger_pad_pressure" : 1,
"l_finger_periph_pressure" : 1,
"l_finger_pad_pressure" : 1,
"gripper_pose" : 7}
self.percept_mon_list = self.perception_names
self.impact_fgc = FastGaussConvolve(self.SAMPLING_RATE, self.I_FILTER_BEG_DELAY,
self.I_FILTER_CEN_DELAY, self.i_sigma_list)
self.type_fgc = FastGaussConvolve(self.SAMPLING_RATE, self.T_FILTER_BEG_DELAY,
self.T_FILTER_CEN_DELAY, self.t_sigma_list)
self.perception_started = False
# self.detect_ready = False
self.fos = FileOperations()
def load_parameters(self):
self.SAMPLING_RATE = rospy.get_param("/overhead_grasping/data_sampling_rate")
self.PERCEPTION_ORDER= rospy.get_param("/overhead_grasping/perception_order")
# impact parameters
self.I_FILTER_BEG_DELAY = rospy.get_param("/overhead_grasping/i_filter_beg_delay")
self.I_FILTER_CEN_DELAY = rospy.get_param("/overhead_grasping/i_filter_cen_delay")
self.I_MIN_SIGMA = rospy.get_param("/overhead_grasping/i_min_sigma")
self.I_MAX_SIGMA = rospy.get_param("/overhead_grasping/i_max_sigma")
self.I_NUM_SIGMA = rospy.get_param("/overhead_grasping/i_num_sigma")
self.i_sigma_list = np.linspace(self.I_MIN_SIGMA,
self.I_MAX_SIGMA,
self.I_NUM_SIGMA)
self.I_DEGREE_DICT = rospy.get_param("/overhead_grasping/i_degree_dict")
self.i_instance_len = 0
for k in self.PERCEPTION_ORDER:
for coord in self.I_DEGREE_DICT[k]:
for deg in coord:
self.i_instance_len += self.I_NUM_SIGMA
self.I_RTREE_CLASSIFIER = rospy.get_param("/overhead_grasping/i_rtree_classifier")
# collision type parameters
self.T_FILTER_BEG_DELAY = rospy.get_param("/overhead_grasping/t_filter_beg_delay")
self.T_FILTER_CEN_DELAY = rospy.get_param("/overhead_grasping/t_filter_cen_delay")
self.T_MIN_SIGMA = rospy.get_param("/overhead_grasping/t_min_sigma")
self.T_MAX_SIGMA = rospy.get_param("/overhead_grasping/t_max_sigma")
self.T_NUM_SIGMA = rospy.get_param("/overhead_grasping/t_num_sigma")
self.t_sigma_list = np.linspace(self.T_MIN_SIGMA,
self.T_MAX_SIGMA,
self.T_NUM_SIGMA)
self.T_DEGREE_DICT = rospy.get_param("/overhead_grasping/t_degree_dict")
self.t_instance_len = 0
for k in self.PERCEPTION_ORDER:
for coord in self.T_DEGREE_DICT[k]:
for deg in coord:
self.t_instance_len += self.T_NUM_SIGMA
self.T_RTREE_CLASSIFIER = rospy.get_param("/overhead_grasping/t_rtree_classifier")
self.STATIC_SIGMA_INDEX = rospy.get_param("/overhead_grasping/static_sigma_index")
self.STATIC_DERIV_THRESH = rospy.get_param("/overhead_grasping/static_deriv_thresh")
##
# Initializes the listeners on the sensor topics. This must
# be started before any data can be collected from the arms.
def activate_sensing(self, tf_listener=None):
log("Initializing arm perception listeners")
self.tf_listener = tf_listener
if "accelerometer" in self.percept_mon_list:
accel_listener = GenericListener("accel_mon_node", AccelerometerState,
"accelerometer/" + self.armc + "_gripper_motor",
self.SAMPLING_RATE, accel_state_processor)
self.perceptions["accelerometer"] = accel_listener.read
if "joint_angles" in self.percept_mon_list:
joint_angles_processor = ft.partial(joints_state_processor,
right_arm=self.is_right_arm,
angles_velocities_efforts=0)
joint_angles_listener = GenericListener("joint_angles_mon_node", JointState,
"joint_states", self.SAMPLING_RATE, joint_angles_processor)
self.perceptions["joint_angles"] = joint_angles_listener.read
if "joint_velocities" in self.percept_mon_list:
joint_velocities_processor = ft.partial(joints_state_processor,
right_arm=self.is_right_arm,
angles_velocities_efforts=1)
joint_velocities_listener = GenericListener("joint_vels_mon_node", JointState,
"joint_states", self.SAMPLING_RATE, joint_velocities_processor)
self.perceptions["joint_velocities"] = joint_velocities_listener.read
if "joint_efforts" in self.percept_mon_list:
joint_efforts_processor = ft.partial(joints_state_processor,
right_arm=self.is_right_arm,
angles_velocities_efforts=2)
joint_efforts_listener = GenericListener("joint_efforts_mon_node", JointState,
"joint_states", self.SAMPLING_RATE, joint_efforts_processor)
self.perceptions["joint_efforts"] = joint_efforts_listener.read
if "r_finger_periph_pressure" in self.percept_mon_list:
r_finger_periph_pressure_processor = ft.partial(pressure_state_sum_processor,
right_finger_tip=True, indicies=range(1,7))
r_finger_periph_pressure_listener = GenericListener(self.armc + "_pressure_r_periph_mon_node", PressureState,
"pressure/" + self.armc + "_gripper_motor", self.SAMPLING_RATE,
r_finger_periph_pressure_processor)
self.perceptions["r_finger_periph_pressure"] = r_finger_periph_pressure_listener.read
if "r_finger_pad_pressure" in self.percept_mon_list:
r_finger_pad_pressure_processor = ft.partial(pressure_state_sum_processor,
right_finger_tip=True, indicies=range(7,22))
r_finger_pad_pressure_listener = GenericListener(self.armc + "_pressure_r_pad_mon_node", PressureState,
"pressure/" + self.armc + "_gripper_motor", self.SAMPLING_RATE,
r_finger_pad_pressure_processor)
self.perceptions["r_finger_pad_pressure"] = r_finger_pad_pressure_listener.read
if "l_finger_periph_pressure" in self.percept_mon_list:
l_finger_periph_pressure_processor = ft.partial(pressure_state_sum_processor,
right_finger_tip=False, indicies=range(1,7))
l_finger_periph_pressure_listener = GenericListener(self.armc + "_pressure_l_periph_mon_node", PressureState,
"pressure/" + self.armc + "_gripper_motor", self.SAMPLING_RATE,
l_finger_periph_pressure_processor)
self.perceptions["l_finger_periph_pressure"] = l_finger_periph_pressure_listener.read
if "l_finger_pad_pressure" in self.percept_mon_list:
l_finger_pad_pressure_processor = ft.partial(pressure_state_sum_processor,
right_finger_tip=False, indicies=range(7,22))
l_finger_pad_pressure_listener = GenericListener(self.armc + "_pressure_l_pad_mon_node", PressureState,
"pressure/" + self.armc + "_gripper_motor", self.SAMPLING_RATE,
l_finger_pad_pressure_processor)
self.perceptions["l_finger_pad_pressure"] = l_finger_pad_pressure_listener.read
for k in self.perceptions:
# Make sure we have recieved a message first
self.perceptions[k](allow_duplication=False, willing_to_wait=True)
self.perceptions[k] = ft.partial(self.perceptions[k], willing_to_wait=False,
quiet=True,
warn=False, allow_duplication=True)
if "gripper_pose" in self.percept_mon_list:
def gripper_pose_lookup():
if self.tf_listener is not None:
lct = self.tf_listener.getLatestCommonTime("/torso_lift_link", "/" + self.armc + "_wrist_roll_link")
pos, quat = self.tf_listener.lookupTransform("/torso_lift_link", "/" + self.armc + "_wrist_roll_link", lct)
return (lct.to_sec(), np.array(pos + quat))
else:
return (0., np.array([0.]*7))
self.perceptions["gripper_pose"] = gripper_pose_lookup
# all callbacks should return data in this format:
# (t, np.array([x1, x2, ...]))
# t is time in seconds
self.clear_vars()
self.perception_started = True
log("Finished initialization")
##
# Initialize variables
def clear_vars(self):
self.datasets = {}
self.models = {}
for k in self.perceptions:
self.datasets[k] = []
self.models[k] = {"mean" : None, "variance" : None}
##
# Begin capturing peception data for all of the listeners
#
# @param duration If None, continue capturing until stop is called.
# Else, stop capturing after duration seconds have passed.
def start_data_capture(self, duration=None):
if not self.perception_started:
err("Perception hasn't been started!")
return False
self.logger = RateCaller(self._gather_perception, self.SAMPLING_RATE)
for k in self.perceptions:
self.datasets[k] += [[]]
self.logger.run()
if not duration is None:
threading.Timer(self.stop_data_capture, duration)
return True
def _gather_perception(self):
for k in self.perceptions:
self.datasets[k][-1].append(self.perceptions[k]())
##
# Stop capturing perception data. Store output data in datasets list for
# later statistics.
def stop_data_capture(self):
self.logger.stop()
num_datapts = len(self.datasets[self.datasets.keys()[0]][-1])
log("%d datapoints collected", num_datapts)
return num_datapts
##
# do processing of signals in current coord group
# sigs_proc contains a list of three signal sets
# each corresponds to a list of convolutions of the signals
# with a degree derivative of a gaussian with various
# scales
def process_signals(self, datasets):
models = {}
sum, num = 0., 0.
for p in datasets:
models[p] = {}
i_sigs_proc = [ [[] for x in range(self.I_NUM_SIGMA)] for y in range(3)]
t_sigs_proc = [ [[] for x in range(self.T_NUM_SIGMA)] for y in range(3)]
data_list = datasets[p]
num_coords = len(data_list[0][0][1])
times = None
for coord in range(num_coords):
stream = data_list[0]
def conv(v):
if type(v) == type([]) or type(v) == type(np.array([])):
return v[0]
return v
signal = [conv(v) for v in zip(*zip(*stream)[1])[coord]]
time_ind = np.array(zip(*stream)[0], dtype=np.float64)
# normalize time indicies by offsetting to first value
time_ind_beg = time_ind[0]
for i, v in enumerate(time_ind):
time_ind[i] = float(time_ind[i] - time_ind_beg)
times = time_ind
for deg in range(3):
for sigma_ind, sigma in enumerate(self.i_sigma_list):
s = self.impact_fgc.convolve_signal(signal, deg, sigma_ind)
i_sigs_proc[deg][sigma_ind].append(s)
for sigma_ind, sigma in enumerate(self.t_sigma_list):
s = self.type_fgc.convolve_signal(signal, deg, sigma_ind)
t_sigs_proc[deg][sigma_ind].append(s)
if rospy.is_shutdown():
sys.exit()
models[p]["impact_features"] = [ [[] for x in range(self.I_NUM_SIGMA)] for y in range(3)]
models[p]["type_features"] = [ [[] for x in range(self.T_NUM_SIGMA)] for y in range(3)]
for deg in range(3):
for sigma in range(self.I_NUM_SIGMA):
models[p]["impact_features"][deg][sigma] = zip(*i_sigs_proc[deg][sigma])
for sigma in range(self.T_NUM_SIGMA):
models[p]["type_features"][deg][sigma] = zip(*t_sigs_proc[deg][sigma])
models[p]["time"] = times
models[p]["i_sigma_list"] = self.i_sigma_list
models[p]["t_sigma_list"] = self.t_sigma_list
return models
# def ready_collision_detection(self):
# # wait for the classifiers to finish loading
# self.impact_classifier.finish_loading()
# # self.coll_type_classifier.finish_loading()
# self.detect_ready = True
##
# Begin monitoring peception data to make sure it doesn't deviate from
# the model provided.
#
# TODO DOCS
# @param duration If None, continue capturing until stop is called.
# Else, stop capturing after duration seconds have passed.
def begin_collision_detection(self, dynamic_detection=False,
callback=None, static_collision_type=None,
debug=False):
if not self.perception_started:
err("Perception hasn't been started!")
return False
# if not self.detect_ready:
# err("ready_collision_detection hasn't been called")
# return False
self.is_dynamic = dynamic_detection
self.callback = callback
if not dynamic_detection:
self._setup_collision_type(static_collision_type)
self.collision_detected = False
self.collision_finished = False
self.debug = debug
self.collision_type = "No collision"
self.cur_iter = 0
self.cur_inds = {}
for k in self.perceptions:
self.cur_inds[k] = 0
self.i_data_buffers = {}
self.t_data_buffers = {}
for k in self.perceptions:
self.i_data_buffers[k] = [ np.zeros((self.impact_fgc.filter_len, self.impact_fgc.filter_len)) for i in range(self.perception_lengths[k])]
self.t_data_buffers[k] = [ np.zeros((self.type_fgc.filter_len, self.type_fgc.filter_len)) for i in range(self.perception_lengths[k])]
self.end_monitor_lock = threading.Lock()
self.data_thread_spawner_lock1 = threading.Lock()
self.data_thread_spawner_lock2 = threading.Lock()
self.data_thread_spawner = RateCaller(self._data_spawn_thread, self.SAMPLING_RATE )
self.data_thread_spawner.run()
self.beg_time = rospy.Time.now().to_sec()
self.t_sum, self.t_num = 0., 0.
self.normal_dict_counts = []
self.temp_dict = {}
for k in self.perceptions:
self.temp_dict[k] = [[] for i in range(self.perception_lengths[k])]
return True
def _setup_collision_type(self, static_collision_type):
if static_collision_type == "all":
static_dict = {"accelerometer" : range(3),
"joint_angles" : range(7),
"joint_velocities" : range(7),
"joint_efforts" : range(7),
"r_finger_periph_pressure" : range(1),
"r_finger_pad_pressure" : range(1),
"l_finger_periph_pressure" : range(1),
"l_finger_pad_pressure" : range(1),
"gripper_pose" : range(7)}
elif static_collision_type in self.perception_names:
static_dict = {static_collision_type : range(
self.perception_lengths[static_collision_type])}
elif static_collision_type == "pressure":
static_dict = {"r_finger_periph_pressure" : range(1),
"r_finger_pad_pressure" : range(1),
"l_finger_periph_pressure" : range(1),
"l_finger_pad_pressure" : range(1)}
elif type(static_collision_type) == type({}):
static_dict = static_collision_type
else:
err("Bad static_collision_type")
self.static_dict = static_dict
def _data_spawn_thread(self):
# only one thread allowed to wait at a time
# all other threads will pass through
if self.data_thread_spawner_lock1.acquire(False):
if self.data_thread_spawner_lock2.acquire(True):
self.data_thread_spawner_lock1.acquire(False)
self.data_thread_spawner_lock1.release()
mt = self.DataThread(self)
mt.start()
class DataThread(threading.Thread):
def __init__(self, apm):
threading.Thread.__init__(self)
self.apm = apm
def run(self):
self.apm._monitor_data_collector()
self.apm.data_thread_spawner_lock2.acquire(False)
self.apm.data_thread_spawner_lock2.release()
def _monitor_data_collector(self):
st_time = rospy.Time.now().to_sec()
#########################################
if rospy.is_shutdown():
self._trigger_collision()
self._finish_collision("rospy shutdown")
add_data = []
for k in self.perceptions:
p_vals = self.perceptions[k]()[1]
for i, v in enumerate(p_vals):
# populate impact collision buffers
for b in range(self.impact_fgc.filter_len - 1, -1, -1):
self.i_data_buffers[k][i][(
b - self.cur_iter) % self.impact_fgc.filter_len, b] = v
if self.debug:
if b == 0 and k == "joint_angles": # and ((i == 0) or (i == 1)):
print v,
# populate collision type detection buffers
for b in range(self.type_fgc.filter_len - 1, -1, -1):
self.t_data_buffers[k][i][(
b - self.cur_iter) % self.type_fgc.filter_len, b] = v
if self.debug:
print
if self.is_dynamic:
if not self.collision_detected:
self._dynamic_collision_detection()
else:
self._dynamic_collision_classifying()
else:
self._static_collision_detection()
self.cur_iter += 1
#########################################
end_time = rospy.Time.now().to_sec()
self.t_sum += end_time - st_time
self.t_num += 1.
def _dynamic_collision_detection(self):
if self.cur_iter < self.impact_fgc.filter_len:
return
instance = np.zeros((self.i_instance_len, 1))
i_place = 0
for k in self.PERCEPTION_ORDER:
for coord in range(len(self.i_data_buffers[k])):
for deg in range(3):
if deg not in self.I_DEGREE_DICT[k][coord]:
continue
for sigma_i, sigma_t in enumerate(self.i_sigma_list):
val = self.impact_fgc.convolve_pt(
self.i_data_buffers[k][coord][(
self.cur_iter % self.impact_fgc.filter_len)],
deg, sigma_i)
instance[i_place, 0] = val
i_place += 1
has_collided = self.impact_classifier.predict(instance)
if has_collided and not self.debug:
if not self.collision_detected:
self._trigger_collision()
def _dynamic_collision_classifying(self):
if self.cur_iter < self.type_fgc.filter_len:
# TODO HANDLE THIS CASE
return
if ((self.cur_iter - self.coll_iter) > self.type_fgc.filter_len / 2):
return
instance = np.zeros((self.t_instance_len, 1))
i_place = 0
for k in self.PERCEPTION_ORDER:
for coord in range(len(self.t_data_buffers[k])):
for deg in range(3):
if deg not in self.T_DEGREE_DICT[k][coord]:
continue
for sigma_i, sigma_t in enumerate(self.t_sigma_list):
val = self.type_fgc.convolve_pt(
self.t_data_buffers[k][coord][(
self.cur_iter % self.type_fgc.filter_len)],
deg, sigma_i)
instance[i_place, 0] = val
i_place += 1
collision_class = self.coll_type_classifier.predict(instance)
if collision_class == 1.:
type = "Environmental collision"
else:
type = "Table collision"
self._finish_collision(type)
def _static_collision_detection(self):
if self.cur_iter < self.impact_fgc.filter_len:
return
for k in self.PERCEPTION_ORDER:
if k not in self.static_dict:
continue
for coord in self.static_dict[k]:
if k == "gripper_pose" and coord >= 3:
# Quaternions are unreliable, this should be fixed
# at some point
if self.debug:
self.temp_dict[k][coord].append(0.)
continue
val = self.impact_fgc.convolve_pt(self.i_data_buffers[k][coord][self.cur_iter % self.impact_fgc.filter_len], 1, self.STATIC_SIGMA_INDEX)
if self.debug:
self.temp_dict[k][coord].append(val)
if (np.fabs(val) >= self.STATIC_DERIV_THRESH[k][coord]
and not self.debug):
print "Collision:", val, "Thresh:", self.STATIC_DERIV_THRESH[k][coord]
self._trigger_collision()
self._finish_collision(k + " collision", coord)
# if k == "r_finger_pad_pressure" and random.randint(0,40) == 0:
# print val
def _trigger_collision(self):
self.collision_detected = True
self.coll_iter = self.cur_iter - self.impact_fgc.filter_len / 2
if not self.callback is None:
self.callback()
def _finish_collision(self, type = "Unlabled", coord = 0):
if not self.collision_finished:
print '-' * 60
print
print ' %s detected' % type
print
print '-' * 60
log("%s reported" % type)
self.collision_type = type
self.collision_coord = coord
if not self.callback is None:
self.callback()
self.collision_finished = True
self.end_collision_detection()
##
# Stop capturing perception data. Store output data in datasets list for
# later statistics.
def end_collision_detection(self):
while (not self.collision_finished and self.collision_detected
and not rospy.is_shutdown()):
rospy.sleep(0.01)
with self.end_monitor_lock:
if not self.data_thread_spawner is None:
print "Avg cvpt: %1.9f" % (self.t_sum / self.t_num)
print "cvpt sum: %1.5f" % (self.t_sum)
print self.t_num
self.data_thread_spawner.stop()
self.data_thread_spawner_lock1.acquire(False)
self.data_thread_spawner_lock1.release()
self.data_thread_spawner_lock2.acquire(False)
self.data_thread_spawner_lock2.release()
self.data_thread_spawner = None
return
def __init__(self, arm, tf_listener=None, rate=0.001,
percept_mon_list=None, model_zeros=None):
log("Initializing arm perception listeners")
self.rate = rate
if arm == 0:
armc = "r"
is_right_arm = True
else:
armc = "l"
is_right_arm = False
self.model_zeros = model_zeros
self.perceptions = {}
self.perception_names = [ "accelerometer",
"joint_angles",
"joint_velocities",
"joint_efforts",
"r_finger_periph_pressure",
"r_finger_pad_pressure",
"l_finger_periph_pressure",
"l_finger_pad_pressure",
"gripper_pose"]
if percept_mon_list is None:
percept_mon_list = self.perception_names
if "accelerometer" in percept_mon_list:
accel_listener = GenericListener("accel_mon_node", AccelerometerState,
"accelerometer/" + armc + "_gripper_motor",
rate, accel_state_processor)
self.perceptions["accelerometer"] = accel_listener.read
if "joint_angles" in percept_mon_list:
joint_angles_processor = ft.partial(joints_state_processor,
right_arm=is_right_arm,
angles_velocities_efforts=0)
joint_angles_listener = GenericListener("joint_angles_mon_node", JointState,
"joint_states", rate, joint_angles_processor)
self.perceptions["joint_angles"] = joint_angles_listener.read
if "joint_velocities" in percept_mon_list:
joint_velocities_processor = ft.partial(joints_state_processor,
right_arm=is_right_arm,
angles_velocities_efforts=1)
joint_velocities_listener = GenericListener("joint_efforts_mon_node", JointState,
"joint_states", rate, joint_velocities_processor)
self.perceptions["joint_velocities"] = joint_velocities_listener.read
if "joint_efforts" in percept_mon_list:
joint_efforts_processor = ft.partial(joints_state_processor,
right_arm=is_right_arm,
angles_velocities_efforts=2)
joint_efforts_listener = GenericListener("joint_efforts_mon_node", JointState,
"joint_states", rate, joint_efforts_processor)
self.perceptions["joint_efforts"] = joint_efforts_listener.read
if "r_finger_periph_pressure" in percept_mon_list:
r_finger_periph_pressure_processor = ft.partial(pressure_state_processor,
right_finger_tip=True, indicies=range(1,7))
r_finger_periph_pressure_listener = GenericListener("pressure_mon_node", PressureState,
"pressure/" + armc + "_gripper_motor", rate,
r_finger_periph_pressure_processor)
self.perceptions["r_finger_periph_pressure"] = r_finger_periph_pressure_listener.read
if "r_finger_pad_pressure" in percept_mon_list:
r_finger_pad_pressure_processor = ft.partial(pressure_state_processor,
right_finger_tip=True, indicies=range(7,22))
r_finger_pad_pressure_listener = GenericListener("pressure_mon_node", PressureState,
"pressure/" + armc + "_gripper_motor", rate,
r_finger_pad_pressure_processor)
self.perceptions["r_finger_pad_pressure"] = r_finger_pad_pressure_listener.read
if "l_finger_periph_pressure" in percept_mon_list:
l_finger_periph_pressure_processor = ft.partial(pressure_state_processor,
right_finger_tip=False, indicies=range(1,7))
l_finger_periph_pressure_listener = GenericListener("pressure_mon_node", PressureState,
"pressure/" + armc + "_gripper_motor", rate,
l_finger_periph_pressure_processor)
self.perceptions["l_finger_periph_pressure"] = l_finger_periph_pressure_listener.read
if "l_finger_pad_pressure" in percept_mon_list:
l_finger_pad_pressure_processor = ft.partial(pressure_state_processor,
right_finger_tip=False, indicies=range(7,22))
l_finger_pad_pressure_listener = GenericListener("pressure_mon_node", PressureState,
"pressure/" + armc + "_gripper_motor", rate,
l_finger_pad_pressure_processor)
self.perceptions["l_finger_pad_pressure"] = l_finger_pad_pressure_listener.read
for k in self.perceptions:
self.perceptions[k] = ft.partial(self.perceptions[k], willing_to_wait=False,
quiet=True,
warn=False, allow_duplication=True)
if "gripper_pose" in percept_mon_list:
def gripper_pose_lookup():
lct = tf_listener.getLatestCommonTime("/torso_lift_link", "/" + armc + "_wrist_roll_link")
pos, quat = tf_listener.lookupTransform("/torso_lift_link", "/" + armc + "_wrist_roll_link", lct)
return (lct.to_nsec(), np.array(pos + quat))
self.perceptions["gripper_pose"] = gripper_pose_lookup
# all callbacks should return data in this format:
# (t, np.array([x1, x2, ...]))
# t is time in nanoseconds
self.clear_vars()
self.logger = RateCaller(self._gather_perception, self.rate)
log("Finished initialization")
class ArmPerceptionMonitor( ):
##
# Initializes the listeners on the perception topics
#
# @param arm 0 if right, 1 if left
# @param rate the rate at which the perception should capture states
# @param percept_mon_list list of perceptions to monitor; if None, do all
def __init__(self, arm, tf_listener=None, rate=0.001,
percept_mon_list=None, model_zeros=None):
log("Initializing arm perception listeners")
self.rate = rate
if arm == 0:
armc = "r"
is_right_arm = True
else:
armc = "l"
is_right_arm = False
self.model_zeros = model_zeros
self.perceptions = {}
self.perception_names = [ "accelerometer",
"joint_angles",
"joint_velocities",
"joint_efforts",
"r_finger_periph_pressure",
"r_finger_pad_pressure",
"l_finger_periph_pressure",
"l_finger_pad_pressure",
"gripper_pose"]
if percept_mon_list is None:
percept_mon_list = self.perception_names
if "accelerometer" in percept_mon_list:
accel_listener = GenericListener("accel_mon_node", AccelerometerState,
"accelerometer/" + armc + "_gripper_motor",
rate, accel_state_processor)
self.perceptions["accelerometer"] = accel_listener.read
if "joint_angles" in percept_mon_list:
joint_angles_processor = ft.partial(joints_state_processor,
right_arm=is_right_arm,
angles_velocities_efforts=0)
joint_angles_listener = GenericListener("joint_angles_mon_node", JointState,
"joint_states", rate, joint_angles_processor)
self.perceptions["joint_angles"] = joint_angles_listener.read
if "joint_velocities" in percept_mon_list:
joint_velocities_processor = ft.partial(joints_state_processor,
right_arm=is_right_arm,
angles_velocities_efforts=1)
joint_velocities_listener = GenericListener("joint_efforts_mon_node", JointState,
"joint_states", rate, joint_velocities_processor)
self.perceptions["joint_velocities"] = joint_velocities_listener.read
if "joint_efforts" in percept_mon_list:
joint_efforts_processor = ft.partial(joints_state_processor,
right_arm=is_right_arm,
angles_velocities_efforts=2)
joint_efforts_listener = GenericListener("joint_efforts_mon_node", JointState,
"joint_states", rate, joint_efforts_processor)
self.perceptions["joint_efforts"] = joint_efforts_listener.read
if "r_finger_periph_pressure" in percept_mon_list:
r_finger_periph_pressure_processor = ft.partial(pressure_state_processor,
right_finger_tip=True, indicies=range(1,7))
r_finger_periph_pressure_listener = GenericListener("pressure_mon_node", PressureState,
"pressure/" + armc + "_gripper_motor", rate,
r_finger_periph_pressure_processor)
self.perceptions["r_finger_periph_pressure"] = r_finger_periph_pressure_listener.read
if "r_finger_pad_pressure" in percept_mon_list:
r_finger_pad_pressure_processor = ft.partial(pressure_state_processor,
right_finger_tip=True, indicies=range(7,22))
r_finger_pad_pressure_listener = GenericListener("pressure_mon_node", PressureState,
"pressure/" + armc + "_gripper_motor", rate,
r_finger_pad_pressure_processor)
self.perceptions["r_finger_pad_pressure"] = r_finger_pad_pressure_listener.read
if "l_finger_periph_pressure" in percept_mon_list:
l_finger_periph_pressure_processor = ft.partial(pressure_state_processor,
right_finger_tip=False, indicies=range(1,7))
l_finger_periph_pressure_listener = GenericListener("pressure_mon_node", PressureState,
"pressure/" + armc + "_gripper_motor", rate,
l_finger_periph_pressure_processor)
self.perceptions["l_finger_periph_pressure"] = l_finger_periph_pressure_listener.read
if "l_finger_pad_pressure" in percept_mon_list:
l_finger_pad_pressure_processor = ft.partial(pressure_state_processor,
right_finger_tip=False, indicies=range(7,22))
l_finger_pad_pressure_listener = GenericListener("pressure_mon_node", PressureState,
"pressure/" + armc + "_gripper_motor", rate,
l_finger_pad_pressure_processor)
self.perceptions["l_finger_pad_pressure"] = l_finger_pad_pressure_listener.read
for k in self.perceptions:
self.perceptions[k] = ft.partial(self.perceptions[k], willing_to_wait=False,
quiet=True,
warn=False, allow_duplication=True)
if "gripper_pose" in percept_mon_list:
def gripper_pose_lookup():
lct = tf_listener.getLatestCommonTime("/torso_lift_link", "/" + armc + "_wrist_roll_link")
pos, quat = tf_listener.lookupTransform("/torso_lift_link", "/" + armc + "_wrist_roll_link", lct)
return (lct.to_nsec(), np.array(pos + quat))
self.perceptions["gripper_pose"] = gripper_pose_lookup
# all callbacks should return data in this format:
# (t, np.array([x1, x2, ...]))
# t is time in nanoseconds
self.clear_vars()
self.logger = RateCaller(self._gather_perception, self.rate)
log("Finished initialization")
def _gather_perception(self):
t1 = rospy.Time.now().to_sec()
for k in self.perceptions:
self.datasets[k][-1] += [self.perceptions[k]()]
t2 = rospy.Time.now().to_sec()
import random
if random.randint(0,100)==0:
print "Time:", t1-t2
##
# Initialize variables
def clear_vars(self):
self.datasets = {}
self.models = {}
for k in self.perceptions:
self.datasets[k] = []
self.models[k] = {"mean" : None, "variance" : None}
self.active = False
##
# Begin capturing peception data for all of the listeners
#
# @param duration If None, continue capturing until stop is called.
# Else, stop capturing after duration seconds have passed.
def start_training(self, duration=None):
if self.active:
log("Perception already active.")
return
self.active = True
for k in self.perceptions:
self.datasets[k] += [[]]
self.logger.run()
if not duration is None:
threading.Timer(self.stop_training, duration)
##
# Stop capturing perception data. Store output data in datasets list for
# later statistics.
def stop_training(self):
if not self.active:
log("Nothing to stop.")
return
self.logger.stop()
self.active = False
return len(self.datasets[self.datasets.keys()[0]][-1])
##
# Save training data as a pickle with given filename
#
# @param filename name of the pickle
def save(self, filename):
save_pickle((self.datasets, self.models), filename)
##
# Load training data as a pickle with given filename
#
# @param filename name of the pickle
def load(self, filename):
self.datasets, self.models = load_pickle(filename)
##
# Generates model functions of all the perceptions over several
# identical trajectories. Each of the parameters is a dictionary
# directing perceptions to their parameters.
#
# @param smooth_wind_dict the window size of the smoothing function
# @param var_wind_dict window size of the variance function
# @param var_smooth_wind_dict window size of the smoothing function on the variance
# @return mean function, variance function
# def generate_models(self, smooth_wind_dict=None, var_wind_dict=None):
# smooth_wind_default = 234
# var_wind_default = 400
# var_smooth_wind = 143
# for perception in self.perceptions:
# data_list = self.datasets[perception]
#
# if data_list is None:
# log("No data to generate model for")
# return None
# if self.active:
# log("Perception already active.")
# return None
# # get the minimum model length
# lens = [len(m) for m in data_list]
# max_len = np.max(lens)
# # dynamic finding of parameters
# if smooth_wind_dict is None or smooth_wind_dict[perception] is None:
# smooth_wind = smooth_wind_default
# else:
# smooth_wind = smooth_wind_dict[perception]
# ret_means, ret_vars, ret_mean_models, ret_noise_vars = [], [], [], []
# ret_times, noise_vars = [], []
# # find the number of coordinates from the first element
# num_coords = len(data_list[0][0][1])
# for coord in range(num_coords):
# mean_models, variance_models = [], []
# times = None
# for stream in data_list:
# # extract only the data stream for a single coordinate (all x values)
# stream_coord = np.array(zip(*zip(*stream)[1])[coord])
# cur_mean_model = signal_smooth(stream_coord, smooth_wind)
# mean_models += [cur_mean_model]
#
# # sum up the squared difference over the whole model
# noise_vars += [ ( sum([(x - y) ** 2 for x,y in zip(cur_mean_model,stream_coord)]) /
# len(cur_mean_model) ) ]
# # find the average case over the several runs
# avg_means_model = np.array([0.] * max_len)
# for i in range(max_len):
# n = 0
# for j in range(len(mean_models)):
# if i < len(mean_models[j]):
# avg_means_model[i] += mean_models[j][i]
# n += 1
# avg_means_model[i] /= n
# if var_wind_dict is None or var_wind_dict[perception] is None:
# var_wind = var_wind_default
# else:
# var_wind = var_wind_dict[perception]
# # find the variance of the signal but use var_wind points around the centers
# # to increase the sample size
# vars_model = signal_list_variance(mean_models, avg_means_model, var_wind)
# vars_model = signal_smooth(vars_model, var_smooth_wind)
# vars_model = signal_smooth(vars_model, var_smooth_wind + 23)
# ret_times += [times]
# ret_means += [avg_means_model]
# ret_vars += [vars_model]
# ret_mean_models += [mean_models]
# ret_noise_vars += [np.average(noise_vars)]
# # TODO deal with timestamp data in some way?
# # self.models[perception]["time"] = ret_times
# self.models[perception]["mean"] = np.array(zip(*ret_means))
# self.models[perception]["variance"] = np.array(zip(*ret_vars))
# a = ret_mean_models
# b = []
# for stream in range(len(a[0])):
# t1 = []
# for val in range(len(a[0][0])):
# t2 = []
# for coord in range(len(a)):
# if val < len(a[coord][stream]):
# t2 += [a[coord][stream][val]]
# t1 += [np.array(t2)]
# b += [t1]
# self.models[perception]["smoothed_signals"] = b
# self.models[perception]["noise_variance"] = np.array(ret_noise_vars)
# return self.models
def get_zeros(self, time=4.):
self._zeros = {}
for k in self.perceptions:
self._zeros[k] = None
self._n = 0
monitor = RateCaller(self._sum_values, self.rate)
monitor.run()
rospy.sleep(time)
monitor.stop()
for k in self.perceptions:
self._zeros[k] /= self._n
return self._zeros
def _sum_values(self):
for k in self.perceptions:
add_data = np.array(self.perceptions[k]()[1])
if self._zeros[k] is None:
self._zeros[k] = np.copy(add_data)
else:
self._zeros[k] += add_data
self._n += 1
##
# Sets up monitoring parameters
def setup_monitoring(self, models,
std_dev_dict=None, noise_dev_dict=None, duration=None,
std_dev_default=2.0, noise_dev_default=0.25,
tol_thresh_dict=None,
contingency=None, window_size=70, current_zeros=None,
sampling_rate = 1,
transform_dict=None, verbose=True, collide=True):
self.std_dev_default = std_dev_default
self.noise_dev_default = noise_dev_default
self.models = models
self.current_data = {}
self.std_dev_dict = std_dev_dict
self.noise_dev_dict = {}
self.contingency = contingency
self.window_size = window_size
self.current_zeros = {}
self.transform_dict = transform_dict
self.tol_thresh_dict = tol_thresh_dict
self.sampling_rate = sampling_rate
self.verbose = verbose
self.collide = collide
self.sum_data = {}
self.cur_pt = 0
self.failure = False
self.dur_timer = None
self.avg_list = {}
self.cum_avg = {}
self.c = {}
self.locks = {}
for k in self.perceptions:
self.current_data[k] = [None] * window_size
self.sum_data[k] = None
self.avg_list[k] = []
self.c[k] = None
self.locks[k] = threading.Lock()
self.create_max_min()
# Checks percept to see if the model indicates a collision with the current
# smoothed perception avg
# Returns index of perception and difference if collision, -1, 0 else
def create_max_min(self):
for k in self.models:
deviation = self.models[k]["variance"]
# noise_deviation = np.sqrt(self.models[k]["noise_variance"])
if self.std_dev_dict is not None and k in self.std_dev_dict:
std_dev = np.array(self.std_dev_dict[k])
else:
std_dev = self.std_dev_default
# if self.noise_dev_dict is not None and k in self.noise_dev_dict:
# noise_dev = np.array(self.noise_dev_dict[k])
# else:
# noise_dev = self.noise_dev_default
if self.tol_thresh_dict is not None and k in self.tol_thresh_dict:
tol_thresh = np.array(self.tol_thresh_dict[k])
else:
tol_thresh = 0.
self.models[k]["dev"] = (std_dev * deviation + tol_thresh)
# self.models[k]["dev"] = (std_dev * deviation + noise_dev * noise_deviation + tol_thresh)
# This is the monitoring equation
self.models[k]["max"] = self.models[k]["mean"] + self.models[k]["dev"]
self.models[k]["min"] = self.models[k]["mean"] - self.models[k]["dev"]
##
# Begin monitoring peception data to make sure it doesn't deviate from
# the model provided.
#
# TODO DOCS
# @param duration If None, continue capturing until stop is called.
# Else, stop capturing after duration seconds have passed.
def begin_monitoring(self, models=None, model_zeros=None,
std_dev_dict=None, noise_dev_dict=None, duration=None,
std_dev_default=2.0, noise_dev_default=0.25,
tol_thresh_dict=None,
contingency=None, window_size=70, current_zeros=None,
sampling_rate = 1,
only_pressure=False,
transform_dict=None, verbose=True, collide=True):
if self.active:
log("Perception already active.")
return
self.active = True
self.setup_monitoring(models,
std_dev_dict=std_dev_dict, noise_dev_dict=noise_dev_dict,
duration=duration, std_dev_default=std_dev_default,
noise_dev_default=noise_dev_default,
tol_thresh_dict=tol_thresh_dict,
contingency=contingency, window_size=window_size,
sampling_rate=sampling_rate,
current_zeros=current_zeros, transform_dict=transform_dict,
verbose=verbose, collide=collide)
# if models is not None:
# self.models = models
if model_zeros is not None:
self.model_zeros = model_zeros
self.cur_pt = 0
self.collision_times = {}
self.collision_sums = {}
self.cur_col_time = 0
self.current_zeros = {}
self.z_sum = {}
self.z_rsum = {}
self.z_list = {}
self.min_prob = 100000.0
self.cur_mals = {}
self.mals = None
self.only_pressure = only_pressure
self.monitor = RateCaller(self._monitor_data, self.rate * self.sampling_rate)
self.monitor.run()
# self.sumcalls = {}
# self.sumsave = {}
if not duration is None:
self.dur_timer = threading.Timer(self.end_monitoring, duration)
self.dur_timer.start()
def _stable_summer(self, percept, data):
# kahanSum
k = percept
# if percept not in self.sumcalls:
# self.sumcalls[percept] = 1
# self.sumsave[percept] = []
# else:
# self.sumcalls[percept] += 1
if self.c[k] is None:
self.c[k] = np.array([0.]*len(data))
if self.sum_data[k] is None:
self.sum_data[k] = np.copy(data)
else:
y = data - self.c[k]
t = self.sum_data[k] + y
self.c[k] = (t - self.sum_data[k]) - y
self.sum_data[k] = t
# self.sumsave[percept] += [self.sum_data[k]]
def _monitor_data(self):
avg_dict = {}
sttime = rospy.Time.now().to_sec()
for k in self.perceptions:
# update the running sum
add_data = self.perceptions[k]()[1]
# apply necessary transforms
# if self.transform_dict is not None:
# if k in self.transform_dict:
# add_data = self.transform_dict[k](add_data)
self._stable_summer(k, add_data)
# If we have enough data to monitor, we check to see if the values are in range
if self.cur_pt >= self.window_size: #self.current_data[k].full():
# self.prev_sum = copy.deepcopy(self.sum_data)
avg_dict[k] = self.sum_data[k] / self.window_size
if not k in self.current_zeros:
if not self.only_pressure:
mean = np.array(self.models[k]["mean"][self.cur_pt * self.sampling_rate])
else:
mean = np.array(self.models[k]["mean"][0])
self.current_zeros[k] = mean - avg_dict[k]
# self.current_zeros[k] = - avg_dict[k]
avg_dict[k] += self.current_zeros[k]
self.avg_list[k] += [avg_dict[k]] * self.sampling_rate
# if self.cur_pt == self.window_size:
# print self.avg_list[k]
# self.prev_avg = copy.deepcopy(avg)
# offset zeros into original perception frame
# if self.current_zeros is not None:
# if False and self.model_zeros is not None:
# avg_dict[k] += self.model_zeros[k] - self.current_zeros[k]
# else:
# # this is hacky, need to use zeros during training instead of first pt
# # print "NOOOOOOOOOOOO!\n"*10
# avg_dict[k] += np.array(self.models[k]["mean"][self.window_size]) - self.current_zeros[k]
# if collision_detected:
# log("Value %d of the perception %s failed with difference %f"
# % (index, k, diff))
# # if diff > 5. and k == "accelerometer":
# # print "avg_list", self.avg_list
# # print "avg", avg
# # for i in range(self.window_size+1):
# # d = self.current_data[k].get()
# # print d
# # self.current_data[k].put(d)
# self.failure = True
# self.contingency()
# self.monitor.stop()
rem_data = self.current_data[k][self.cur_pt % self.window_size]
self._stable_summer(k, -rem_data)
self.current_data[k][self.cur_pt % self.window_size] = add_data
if self.cur_pt >= self.window_size: #self.current_data[k].full():
collision_detected = self.collision_detect(avg_dict)
if self.collide and collision_detected:
print "collision reported"
self.failure = True
if not self.contingency is None:
self.contingency()
self.monitor.stop()
return
self.cur_pt += 1
if not self.only_pressure:
if self.cur_pt * self.sampling_rate >= 1.00 * len(self.models[self.models.keys()[0]]["mean"]):
print "ending early:", self.cur_pt * self.sampling_rate
self.end_monitoring()
endtime = rospy.Time.now().to_sec()
# if self.cur_pt % 10 == 0:
# print "dur:", sttime - endtime
##
# Stop capturing perception data. Store output data in datasets list for
# later statistics.
# TODO DOCS
def end_monitoring(self):
if not self.active:
log("Nothing to stop.")
return self.avg_list
print "-------------------------"
print "z averages:"
self.z_avg = {}
for k in self.z_sum:
self.z_avg[k] = self.z_sum[k] / self.cur_pt
print k, ":", self.z_avg[k]
print "-------------------------"
print "MINIMUM PROB"
print self.min_prob
print "-------------------------"
# print "Sum calls", self.sumcalls
# if self.sumcalls["r_finger_periph_pressure"] == 202:
# print self.sumsave
self.monitor.stop()
if self.dur_timer is not None:
self.dur_timer.cancel()
self.dur_timer = None
self.active = False
return self.avg_list
# Checks percept to see if the model indicates a collision with the current
# smoothed perception avg
# Returns index of perception and difference if collision, -1, 0 else
def collision_detect(self, avg_dict):
# is_outside_range_dict = {}
z = {}
mal_sum = 0.
for k in avg_dict:
# This is the monitoring equation
# is_outside_range_dict[k] = ((avg_dict[k] > self.models[k]["max"][self.cur_pt * self.sampling_rate]) +
# (avg_dict[k] < self.models[k]["min"][self.cur_pt * self.sampling_rate]))
# Uses both variance from various grasp tries and general noise variance
if not self.only_pressure:
mean = self.models[k]["mean"][self.cur_pt * self.sampling_rate]
dev = self.models[k]["dev"][self.cur_pt * self.sampling_rate]
else:
mean = self.models[k]["mean"][0]
dev = self.models[k]["dev"][0]
cur_mal = ((avg_dict[k] - mean) / dev) ** 2
self.cur_mals[k] = cur_mal
mal_sum += np.add.reduce( ( (avg_dict[k] - mean) / dev) ** 2 )
z[k] = np.fabs(avg_dict[k] - mean) / dev
if not k in self.z_sum:
self.z_sum[k] = np.copy(z[k])
else:
self.z_sum[k] += z[k]
# print "incoldet"
# collision_detected = self.collision_filtering(is_outside_range_dict)
# collision_detected = self.collision_filtering_mal(np.sqrt(mal_sum))
# return collision_detected
collision_detected = self.collision_filtering(z)
# print "coldete", collision_detected
return collision_detected
def collision_filtering_mal(self, mal):
prob = 1.
TIME_WINDOW = 4
if self.mals is None:
self.mals = np.array([0.] * TIME_WINDOW)
self.mals[self.cur_pt % TIME_WINDOW] = mal
if self.cur_pt < TIME_WINDOW:
return False
mal_avg = np.add.reduce(self.mals / TIME_WINDOW)
MAL_THRESH = 8.0
if mal_avg > MAL_THRESH:
print "Collision with mal dist:", mal_avg
for k in self.cur_mals:
print k, self.cur_mals[k]
return True
return False
def collision_filtering(self, z):
prob = 1.
TIME_WINDOW = 4
for k in z:
if not k in self.z_rsum:
self.z_rsum[k] = np.copy(z[k])
self.z_list[k] = [np.array([0.]*len(z[k]))] * TIME_WINDOW
else:
self.z_rsum[k] += z[k]
self.z_rsum[k] -= self.z_list[k][self.cur_pt % TIME_WINDOW]
self.z_list[k][self.cur_pt % TIME_WINDOW] = z[k]
# print z[k]
prob *= np.multiply.reduce(2 * stats.norm.cdf(-self.z_rsum[k] / TIME_WINDOW))
# print prob
if self.cur_pt < TIME_WINDOW:
return False
if prob < self.min_prob:
self.min_prob = prob
# CONFIDENCE = 1e-6
if not self.only_pressure:
# 4e-7 50/50
CONFIDENCE = 2.0e-6
else:
# pressure only confidence
CONFIDENCE = 1.e-5
if self.cur_pt % 20 == 0:
print "Current pt:", self.cur_pt, ", probability:", prob
if prob < CONFIDENCE:
print "probability:", prob
print "collision returned"
return True
# print "-------------------------"
# print num_percep_col
# for k in sig_counts:
# if sig_counts[k] >= PERCEPT_SIG_REQ[k]:
# print k, ":", sig_counts[k]
# print "-------------------------"
# return True
return False
# def collision_filtering(self, is_outside_range_dict):
# isord = is_outside_range_dict
# TIME_WINDOW = 10
# NUM_PERCEPT_COL_REQ = 2
# PERCEPT_SIG_REQ = { "accelerometer" : 2,
# "joint_angles" : 2,
# "joint_velocities" : 3,
# "joint_efforts" : 2,
# "r_finger_periph_pressure" : 2,
# "r_finger_pad_pressure" : 5,
# "l_finger_periph_pressure" : 2,
# "l_finger_pad_pressure" : 5 }
# num_percep_col = 0
# sig_counts = {}
# num_periph = 0
# for k in isord:
# # if any(isord[k]):
# # print k, isord
# if not k in self.collision_times:
# self.collision_times[k] = [np.array([0] * len(isord[k]))] * TIME_WINDOW
# self.collision_sums[k] = np.array([0] * len(isord[k]))
# # def queue_sum(q):
# # sum = None
# # for i in range(TIME_WINDOW):
# # val = q.get()
# # if sum is None:
# # sum = np.copy(val)
# # sum += val
# # q.put(val)
# # return sum
# # c_sum = queue_sum(self.collision_times[k])
# self.collision_sums[k] -= self.collision_times[k][self.cur_col_time]
# self.cur_col_time = (self.cur_col_time + 1) % TIME_WINDOW
# self.collision_times[k][self.cur_col_time] = np.int32(np.array(isord[k]))
# self.collision_sums[k] += self.collision_times[k][self.cur_col_time]
# def g0(x):
# if x > 0:
# return 1
# return 0
# sig_count = np.sum(map(g0, self.collision_sums[k]))
# sig_counts[k] = sig_count
# if sig_count >= PERCEPT_SIG_REQ[k]:
# # print "cols", k, isord[k]
# if k == "r_finger_periph_pressure" or k == "l_finger_periph_pressure":
# num_periph += 1
# num_percep_col += 1
# # num_periph != 1 eliminates side object collision possibilities
# if num_percep_col >= NUM_PERCEPT_COL_REQ and num_periph != 1:
# print "collision returned"
# print "-------------------------"
# print num_percep_col
# for k in sig_counts:
# if sig_counts[k] >= PERCEPT_SIG_REQ[k]:
# print k, ":", sig_counts[k]
# print "-------------------------"
# return True
# return False
# if self.collide and any(is_outside_range):
# # sensor has fallen outside acceptable range, trigger
# for i, x in enumerate(is_outside_range):
# if x:
# # print "Average:", avg
# # print "Last avg:", self.prev_avg
# # print "Prev sum:", self.prev_sum
# # print "MOD:", self.model_zeros[k]
# # print "MON:", self.current_zeros[k]
# return i, diff[i]
# return -1, 0.
# @TODO add sampling_rate stuff
def simulate_monitoring(self, monitor_data, models=None, model_zeros=None,
std_dev_dict=None, noise_dev_dict=None,
duration=None,
std_dev_default=2.0, noise_dev_default=0.25,
tol_thresh_dict=None,
contingency=None, window_size=70, current_zeros=None,
transform_dict=None, verbose=True, collide=True):
self.setup_monitoring(std_dev_dict=std_dev_dict, noise_dev_dict=noise_dev_dict,
duration=duration, std_dev_default=std_dev_default,
tol_thresh_dict=tol_thresh_dict,
noise_dev_default=noise_dev_default, contingency=contingency, window_size=window_size,
current_zeros=current_zeros, transform_dict=transform_dict,
verbose=verbose, collide=collide)
if models is not None:
self.models = models
if model_zeros is not None:
self.model_zeros = model_zeros
self.cur_pt = self.window_size
collision = None
# i is the number of samples in the monitor data
for i in range(len(monitor_data[monitor_data.keys()[0]])):
for k in monitor_data:
index, diff = self.collision_detect(k, monitor_data[k][i])
if index != -1:
collision = (k, index, diff)
self.cur_pt += 1
return collision
def resample_and_thin_data(self, monitor_data, sample_rate):
ret_data = {}
for k in monitor_data:
ret_data[k] = {}
ret_data[k]["mean"] = monitor_data[k]["mean"][::sample_rate]
ret_data[k]["variance"] = monitor_data[k]["variance"][::sample_rate]
return ret_data
class ArmPerceptionMonitor():
##
# Initializes internal variables
#
# @param arm 0 if right, 1 if left
# @param percept_mon_list list of perceptions to monitor; if None, do all
def __init__(self, arm):
self.load_parameters()
############## Classifiers #################
self.impact_classifier = cfiers.classifiers_dict[
"small_refined_random_forest"]
self.coll_type_classifier = cfiers.classifiers_dict[
"large_refined_random_forest"]
log("Loading impact classifier")
self.impact_classifier.load(self.I_RTREE_CLASSIFIER)
log("Loading collision type classifier")
self.coll_type_classifier.load(self.T_RTREE_CLASSIFIER)
if arm == 0:
self.armc = "r"
self.is_right_arm = True
else:
self.armc = "l"
self.is_right_arm = False
self.perceptions = {}
self.perception_names = [
"accelerometer", "joint_angles", "joint_velocities",
"joint_efforts", "r_finger_periph_pressure",
"r_finger_pad_pressure", "l_finger_periph_pressure",
"l_finger_pad_pressure", "gripper_pose"
]
self.perception_lengths = {
"accelerometer": 3,
"joint_angles": 7,
"joint_velocities": 7,
"joint_efforts": 7,
"r_finger_periph_pressure": 1,
"r_finger_pad_pressure": 1,
"l_finger_periph_pressure": 1,
"l_finger_pad_pressure": 1,
"gripper_pose": 7
}
self.percept_mon_list = self.perception_names
self.impact_fgc = FastGaussConvolve(self.SAMPLING_RATE,
self.I_FILTER_BEG_DELAY,
self.I_FILTER_CEN_DELAY,
self.i_sigma_list)
self.type_fgc = FastGaussConvolve(self.SAMPLING_RATE,
self.T_FILTER_BEG_DELAY,
self.T_FILTER_CEN_DELAY,
self.t_sigma_list)
self.perception_started = False
# self.detect_ready = False
self.fos = FileOperations()
def load_parameters(self):
self.SAMPLING_RATE = rospy.get_param(
"/overhead_grasping/data_sampling_rate")
self.PERCEPTION_ORDER = rospy.get_param(
"/overhead_grasping/perception_order")
# impact parameters
self.I_FILTER_BEG_DELAY = rospy.get_param(
"/overhead_grasping/i_filter_beg_delay")
self.I_FILTER_CEN_DELAY = rospy.get_param(
"/overhead_grasping/i_filter_cen_delay")
self.I_MIN_SIGMA = rospy.get_param("/overhead_grasping/i_min_sigma")
self.I_MAX_SIGMA = rospy.get_param("/overhead_grasping/i_max_sigma")
self.I_NUM_SIGMA = rospy.get_param("/overhead_grasping/i_num_sigma")
self.i_sigma_list = np.linspace(self.I_MIN_SIGMA, self.I_MAX_SIGMA,
self.I_NUM_SIGMA)
self.I_DEGREE_DICT = rospy.get_param(
"/overhead_grasping/i_degree_dict")
self.i_instance_len = 0
for k in self.PERCEPTION_ORDER:
for coord in self.I_DEGREE_DICT[k]:
for deg in coord:
self.i_instance_len += self.I_NUM_SIGMA
self.I_RTREE_CLASSIFIER = rospy.get_param(
"/overhead_grasping/i_rtree_classifier")
# collision type parameters
self.T_FILTER_BEG_DELAY = rospy.get_param(
"/overhead_grasping/t_filter_beg_delay")
self.T_FILTER_CEN_DELAY = rospy.get_param(
"/overhead_grasping/t_filter_cen_delay")
self.T_MIN_SIGMA = rospy.get_param("/overhead_grasping/t_min_sigma")
self.T_MAX_SIGMA = rospy.get_param("/overhead_grasping/t_max_sigma")
self.T_NUM_SIGMA = rospy.get_param("/overhead_grasping/t_num_sigma")
self.t_sigma_list = np.linspace(self.T_MIN_SIGMA, self.T_MAX_SIGMA,
self.T_NUM_SIGMA)
self.T_DEGREE_DICT = rospy.get_param(
"/overhead_grasping/t_degree_dict")
self.t_instance_len = 0
for k in self.PERCEPTION_ORDER:
for coord in self.T_DEGREE_DICT[k]:
for deg in coord:
self.t_instance_len += self.T_NUM_SIGMA
self.T_RTREE_CLASSIFIER = rospy.get_param(
"/overhead_grasping/t_rtree_classifier")
self.STATIC_SIGMA_INDEX = rospy.get_param(
"/overhead_grasping/static_sigma_index")
self.STATIC_DERIV_THRESH = rospy.get_param(
"/overhead_grasping/static_deriv_thresh")
##
# Initializes the listeners on the sensor topics. This must
# be started before any data can be collected from the arms.
def activate_sensing(self, tf_listener=None):
log("Initializing arm perception listeners")
self.tf_listener = tf_listener
if "accelerometer" in self.percept_mon_list:
accel_listener = GenericListener(
"accel_mon_node", AccelerometerState,
"accelerometer/" + self.armc + "_gripper_motor",
self.SAMPLING_RATE, accel_state_processor)
self.perceptions["accelerometer"] = accel_listener.read
if "joint_angles" in self.percept_mon_list:
joint_angles_processor = ft.partial(joints_state_processor,
right_arm=self.is_right_arm,
angles_velocities_efforts=0)
joint_angles_listener = GenericListener("joint_angles_mon_node",
JointState, "joint_states",
self.SAMPLING_RATE,
joint_angles_processor)
self.perceptions["joint_angles"] = joint_angles_listener.read
if "joint_velocities" in self.percept_mon_list:
joint_velocities_processor = ft.partial(
joints_state_processor,
right_arm=self.is_right_arm,
angles_velocities_efforts=1)
joint_velocities_listener = GenericListener(
"joint_vels_mon_node", JointState, "joint_states",
self.SAMPLING_RATE, joint_velocities_processor)
self.perceptions[
"joint_velocities"] = joint_velocities_listener.read
if "joint_efforts" in self.percept_mon_list:
joint_efforts_processor = ft.partial(joints_state_processor,
right_arm=self.is_right_arm,
angles_velocities_efforts=2)
joint_efforts_listener = GenericListener("joint_efforts_mon_node",
JointState,
"joint_states",
self.SAMPLING_RATE,
joint_efforts_processor)
self.perceptions["joint_efforts"] = joint_efforts_listener.read
if "r_finger_periph_pressure" in self.percept_mon_list:
r_finger_periph_pressure_processor = ft.partial(
pressure_state_sum_processor,
right_finger_tip=True,
indicies=range(1, 7))
r_finger_periph_pressure_listener = GenericListener(
self.armc + "_pressure_r_periph_mon_node", PressureState,
"pressure/" + self.armc + "_gripper_motor", self.SAMPLING_RATE,
r_finger_periph_pressure_processor)
self.perceptions[
"r_finger_periph_pressure"] = r_finger_periph_pressure_listener.read
if "r_finger_pad_pressure" in self.percept_mon_list:
r_finger_pad_pressure_processor = ft.partial(
pressure_state_sum_processor,
right_finger_tip=True,
indicies=range(7, 22))
r_finger_pad_pressure_listener = GenericListener(
self.armc + "_pressure_r_pad_mon_node", PressureState,
"pressure/" + self.armc + "_gripper_motor", self.SAMPLING_RATE,
r_finger_pad_pressure_processor)
self.perceptions[
"r_finger_pad_pressure"] = r_finger_pad_pressure_listener.read
if "l_finger_periph_pressure" in self.percept_mon_list:
l_finger_periph_pressure_processor = ft.partial(
pressure_state_sum_processor,
right_finger_tip=False,
indicies=range(1, 7))
l_finger_periph_pressure_listener = GenericListener(
self.armc + "_pressure_l_periph_mon_node", PressureState,
"pressure/" + self.armc + "_gripper_motor", self.SAMPLING_RATE,
l_finger_periph_pressure_processor)
self.perceptions[
"l_finger_periph_pressure"] = l_finger_periph_pressure_listener.read
if "l_finger_pad_pressure" in self.percept_mon_list:
l_finger_pad_pressure_processor = ft.partial(
pressure_state_sum_processor,
right_finger_tip=False,
indicies=range(7, 22))
l_finger_pad_pressure_listener = GenericListener(
self.armc + "_pressure_l_pad_mon_node", PressureState,
"pressure/" + self.armc + "_gripper_motor", self.SAMPLING_RATE,
l_finger_pad_pressure_processor)
self.perceptions[
"l_finger_pad_pressure"] = l_finger_pad_pressure_listener.read
for k in self.perceptions:
# Make sure we have recieved a message first
self.perceptions[k](allow_duplication=False, willing_to_wait=True)
self.perceptions[k] = ft.partial(self.perceptions[k],
willing_to_wait=False,
quiet=True,
warn=False,
allow_duplication=True)
if "gripper_pose" in self.percept_mon_list:
def gripper_pose_lookup():
if self.tf_listener is not None:
lct = self.tf_listener.getLatestCommonTime(
"/torso_lift_link",
"/" + self.armc + "_wrist_roll_link")
pos, quat = self.tf_listener.lookupTransform(
"/torso_lift_link",
"/" + self.armc + "_wrist_roll_link", lct)
return (lct.to_sec(), np.array(pos + quat))
else:
return (0., np.array([0.] * 7))
self.perceptions["gripper_pose"] = gripper_pose_lookup
# all callbacks should return data in this format:
# (t, np.array([x1, x2, ...]))
# t is time in seconds
self.clear_vars()
self.perception_started = True
log("Finished initialization")
##
# Initialize variables
def clear_vars(self):
self.datasets = {}
self.models = {}
for k in self.perceptions:
self.datasets[k] = []
self.models[k] = {"mean": None, "variance": None}
##
# Begin capturing peception data for all of the listeners
#
# @param duration If None, continue capturing until stop is called.
# Else, stop capturing after duration seconds have passed.
def start_data_capture(self, duration=None):
if not self.perception_started:
err("Perception hasn't been started!")
return False
self.logger = RateCaller(self._gather_perception, self.SAMPLING_RATE)
for k in self.perceptions:
self.datasets[k] += [[]]
self.logger.run()
if not duration is None:
threading.Timer(self.stop_data_capture, duration)
return True
def _gather_perception(self):
for k in self.perceptions:
self.datasets[k][-1].append(self.perceptions[k]())
##
# Stop capturing perception data. Store output data in datasets list for
# later statistics.
def stop_data_capture(self):
self.logger.stop()
num_datapts = len(self.datasets[self.datasets.keys()[0]][-1])
log("%d datapoints collected", num_datapts)
return num_datapts
##
# do processing of signals in current coord group
# sigs_proc contains a list of three signal sets
# each corresponds to a list of convolutions of the signals
# with a degree derivative of a gaussian with various
# scales
def process_signals(self, datasets):
models = {}
sum, num = 0., 0.
for p in datasets:
models[p] = {}
i_sigs_proc = [[[] for x in range(self.I_NUM_SIGMA)]
for y in range(3)]
t_sigs_proc = [[[] for x in range(self.T_NUM_SIGMA)]
for y in range(3)]
data_list = datasets[p]
num_coords = len(data_list[0][0][1])
times = None
for coord in range(num_coords):
stream = data_list[0]
def conv(v):
if type(v) == type([]) or type(v) == type(np.array([])):
return v[0]
return v
signal = [conv(v) for v in zip(*zip(*stream)[1])[coord]]
time_ind = np.array(zip(*stream)[0], dtype=np.float64)
# normalize time indicies by offsetting to first value
time_ind_beg = time_ind[0]
for i, v in enumerate(time_ind):
time_ind[i] = float(time_ind[i] - time_ind_beg)
times = time_ind
for deg in range(3):
for sigma_ind, sigma in enumerate(self.i_sigma_list):
s = self.impact_fgc.convolve_signal(
signal, deg, sigma_ind)
i_sigs_proc[deg][sigma_ind].append(s)
for sigma_ind, sigma in enumerate(self.t_sigma_list):
s = self.type_fgc.convolve_signal(
signal, deg, sigma_ind)
t_sigs_proc[deg][sigma_ind].append(s)
if rospy.is_shutdown():
sys.exit()
models[p]["impact_features"] = [[[]
for x in range(self.I_NUM_SIGMA)]
for y in range(3)]
models[p]["type_features"] = [[[] for x in range(self.T_NUM_SIGMA)]
for y in range(3)]
for deg in range(3):
for sigma in range(self.I_NUM_SIGMA):
models[p]["impact_features"][deg][sigma] = zip(
*i_sigs_proc[deg][sigma])
for sigma in range(self.T_NUM_SIGMA):
models[p]["type_features"][deg][sigma] = zip(
*t_sigs_proc[deg][sigma])
models[p]["time"] = times
models[p]["i_sigma_list"] = self.i_sigma_list
models[p]["t_sigma_list"] = self.t_sigma_list
return models
# def ready_collision_detection(self):
# # wait for the classifiers to finish loading
# self.impact_classifier.finish_loading()
# # self.coll_type_classifier.finish_loading()
# self.detect_ready = True
##
# Begin monitoring peception data to make sure it doesn't deviate from
# the model provided.
#
# TODO DOCS
# @param duration If None, continue capturing until stop is called.
# Else, stop capturing after duration seconds have passed.
def begin_collision_detection(self,
dynamic_detection=False,
callback=None,
static_collision_type=None,
debug=False):
if not self.perception_started:
err("Perception hasn't been started!")
return False
# if not self.detect_ready:
# err("ready_collision_detection hasn't been called")
# return False
self.is_dynamic = dynamic_detection
self.callback = callback
if not dynamic_detection:
self._setup_collision_type(static_collision_type)
self.collision_detected = False
self.collision_finished = False
self.debug = debug
self.collision_type = "No collision"
self.cur_iter = 0
self.cur_inds = {}
for k in self.perceptions:
self.cur_inds[k] = 0
self.i_data_buffers = {}
self.t_data_buffers = {}
for k in self.perceptions:
self.i_data_buffers[k] = [
np.zeros(
(self.impact_fgc.filter_len, self.impact_fgc.filter_len))
for i in range(self.perception_lengths[k])
]
self.t_data_buffers[k] = [
np.zeros((self.type_fgc.filter_len, self.type_fgc.filter_len))
for i in range(self.perception_lengths[k])
]
self.end_monitor_lock = threading.Lock()
self.data_thread_spawner_lock1 = threading.Lock()
self.data_thread_spawner_lock2 = threading.Lock()
self.data_thread_spawner = RateCaller(self._data_spawn_thread,
self.SAMPLING_RATE)
self.data_thread_spawner.run()
self.beg_time = rospy.Time.now().to_sec()
self.t_sum, self.t_num = 0., 0.
self.normal_dict_counts = []
self.temp_dict = {}
for k in self.perceptions:
self.temp_dict[k] = [[] for i in range(self.perception_lengths[k])]
return True
def _setup_collision_type(self, static_collision_type):
if static_collision_type == "all":
static_dict = {
"accelerometer": range(3),
"joint_angles": range(7),
"joint_velocities": range(7),
"joint_efforts": range(7),
"r_finger_periph_pressure": range(1),
"r_finger_pad_pressure": range(1),
"l_finger_periph_pressure": range(1),
"l_finger_pad_pressure": range(1),
"gripper_pose": range(7)
}
elif static_collision_type in self.perception_names:
static_dict = {
static_collision_type:
range(self.perception_lengths[static_collision_type])
}
elif static_collision_type == "pressure":
static_dict = {
"r_finger_periph_pressure": range(1),
"r_finger_pad_pressure": range(1),
"l_finger_periph_pressure": range(1),
"l_finger_pad_pressure": range(1)
}
elif type(static_collision_type) == type({}):
static_dict = static_collision_type
else:
err("Bad static_collision_type")
self.static_dict = static_dict
def _data_spawn_thread(self):
# only one thread allowed to wait at a time
# all other threads will pass through
if self.data_thread_spawner_lock1.acquire(False):
if self.data_thread_spawner_lock2.acquire(True):
self.data_thread_spawner_lock1.acquire(False)
self.data_thread_spawner_lock1.release()
mt = self.DataThread(self)
mt.start()
class DataThread(threading.Thread):
def __init__(self, apm):
threading.Thread.__init__(self)
self.apm = apm
def run(self):
self.apm._monitor_data_collector()
self.apm.data_thread_spawner_lock2.acquire(False)
self.apm.data_thread_spawner_lock2.release()
def _monitor_data_collector(self):
st_time = rospy.Time.now().to_sec()
#########################################
if rospy.is_shutdown():
self._trigger_collision()
self._finish_collision("rospy shutdown")
add_data = []
for k in self.perceptions:
p_vals = self.perceptions[k]()[1]
for i, v in enumerate(p_vals):
# populate impact collision buffers
for b in range(self.impact_fgc.filter_len - 1, -1, -1):
self.i_data_buffers[k][i][(b - self.cur_iter) %
self.impact_fgc.filter_len,
b] = v
if self.debug:
if b == 0 and k == "joint_angles": # and ((i == 0) or (i == 1)):
print v,
# populate collision type detection buffers
for b in range(self.type_fgc.filter_len - 1, -1, -1):
self.t_data_buffers[k][i][(b - self.cur_iter) %
self.type_fgc.filter_len, b] = v
if self.debug:
print
if self.is_dynamic:
if not self.collision_detected:
self._dynamic_collision_detection()
else:
self._dynamic_collision_classifying()
else:
self._static_collision_detection()
self.cur_iter += 1
#########################################
end_time = rospy.Time.now().to_sec()
self.t_sum += end_time - st_time
self.t_num += 1.
def _dynamic_collision_detection(self):
if self.cur_iter < self.impact_fgc.filter_len:
return
instance = np.zeros((self.i_instance_len, 1))
i_place = 0
for k in self.PERCEPTION_ORDER:
for coord in range(len(self.i_data_buffers[k])):
for deg in range(3):
if deg not in self.I_DEGREE_DICT[k][coord]:
continue
for sigma_i, sigma_t in enumerate(self.i_sigma_list):
val = self.impact_fgc.convolve_pt(
self.i_data_buffers[k][coord][(
self.cur_iter % self.impact_fgc.filter_len)],
deg, sigma_i)
instance[i_place, 0] = val
i_place += 1
has_collided = self.impact_classifier.predict(instance)
if has_collided and not self.debug:
if not self.collision_detected:
self._trigger_collision()
def _dynamic_collision_classifying(self):
if self.cur_iter < self.type_fgc.filter_len:
# TODO HANDLE THIS CASE
return
if ((self.cur_iter - self.coll_iter) > self.type_fgc.filter_len / 2):
return
instance = np.zeros((self.t_instance_len, 1))
i_place = 0
for k in self.PERCEPTION_ORDER:
for coord in range(len(self.t_data_buffers[k])):
for deg in range(3):
if deg not in self.T_DEGREE_DICT[k][coord]:
continue
for sigma_i, sigma_t in enumerate(self.t_sigma_list):
val = self.type_fgc.convolve_pt(
self.t_data_buffers[k][coord][(
self.cur_iter % self.type_fgc.filter_len)],
deg, sigma_i)
instance[i_place, 0] = val
i_place += 1
collision_class = self.coll_type_classifier.predict(instance)
if collision_class == 1.:
type = "Environmental collision"
else:
type = "Table collision"
self._finish_collision(type)
def _static_collision_detection(self):
if self.cur_iter < self.impact_fgc.filter_len:
return
for k in self.PERCEPTION_ORDER:
if k not in self.static_dict:
continue
for coord in self.static_dict[k]:
if k == "gripper_pose" and coord >= 3:
# Quaternions are unreliable, this should be fixed
# at some point
if self.debug:
self.temp_dict[k][coord].append(0.)
continue
val = self.impact_fgc.convolve_pt(
self.i_data_buffers[k][coord][self.cur_iter %
self.impact_fgc.filter_len],
1, self.STATIC_SIGMA_INDEX)
if self.debug:
self.temp_dict[k][coord].append(val)
if (np.fabs(val) >= self.STATIC_DERIV_THRESH[k][coord]
and not self.debug):
print "Collision:", val, "Thresh:", self.STATIC_DERIV_THRESH[
k][coord]
self._trigger_collision()
self._finish_collision(k + " collision", coord)
# if k == "r_finger_pad_pressure" and random.randint(0,40) == 0:
# print val
def _trigger_collision(self):
self.collision_detected = True
self.coll_iter = self.cur_iter - self.impact_fgc.filter_len / 2
if not self.callback is None:
self.callback()
def _finish_collision(self, type="Unlabled", coord=0):
if not self.collision_finished:
print '-' * 60
print
print ' %s detected' % type
print
print '-' * 60
log("%s reported" % type)
self.collision_type = type
self.collision_coord = coord
if not self.callback is None:
self.callback()
self.collision_finished = True
self.end_collision_detection()
##
# Stop capturing perception data. Store output data in datasets list for
# later statistics.
def end_collision_detection(self):
while (not self.collision_finished and self.collision_detected
and not rospy.is_shutdown()):
rospy.sleep(0.01)
with self.end_monitor_lock:
if not self.data_thread_spawner is None:
print "Avg cvpt: %1.9f" % (self.t_sum / self.t_num)
print "cvpt sum: %1.5f" % (self.t_sum)
print self.t_num
self.data_thread_spawner.stop()
self.data_thread_spawner_lock1.acquire(False)
self.data_thread_spawner_lock1.release()
self.data_thread_spawner_lock2.acquire(False)
self.data_thread_spawner_lock2.release()
self.data_thread_spawner = None
return