class AbstractPathPlanner:
rbprmBuilder = None
ps = None
v = None
afftool = None
pp = None
extra_dof_bounds = None
robot_node_name = None # name of the robot in the node list of the viewer
def __init__(self):
self.v_max = -1 # bounds on the linear velocity for the root, negative values mean unused
self.a_max = -1 # bounds on the linear acceleration for the root, negative values mean unused
self.root_translation_bounds = [
0
] * 6 # bounds on the root translation position (-x, +x, -y, +y, -z, +z)
self.root_rotation_bounds = [
-3.14, 3.14, -0.01, 0.01, -0.01, 0.01
] # bounds on the rotation of the root (-z, z, -y, y, -x, x)
# The rotation bounds are only used during the random sampling, they are not enforced along the path
self.extra_dof = 6 # number of extra config appended after the joints configuration, 6 to store linear root velocity and acceleration
self.mu = 0.5 # friction coefficient between the robot and the environment
self.used_limbs = [
] # names of the limbs that must be in contact during all the motion
self.size_foot_x = 0 # size of the feet along the x axis
self.size_foot_y = 0 # size of the feet along the y axis
self.q_init = []
self.q_goal = []
@abstractmethod
def load_rbprm(self):
"""
Build an rbprmBuilder instance for the correct robot and initialize it's extra config size
"""
pass
def set_configurations(self):
self.rbprmBuilder.client.robot.setDimensionExtraConfigSpace(
self.extra_dof)
self.q_init = self.rbprmBuilder.getCurrentConfig()
self.q_goal = self.rbprmBuilder.getCurrentConfig()
self.q_init[2] = self.rbprmBuilder.ref_height
self.q_goal[2] = self.rbprmBuilder.ref_height
def compute_extra_config_bounds(self):
"""
Compute extra dof bounds from the current values of v_max and a_max
By default, set symmetrical bounds on x and y axis and bounds z axis values to 0
"""
# bounds for the extradof : by default use v_max/a_max on x and y axis and 0 on z axis
self.extra_dof_bounds = [
-self.v_max, self.v_max, -self.v_max, self.v_max, 0, 0,
-self.a_max, self.a_max, -self.a_max, self.a_max, 0, 0
]
def set_joints_bounds(self):
"""
Set the root translation and rotation bounds as well as the the extra dofs bounds
"""
self.rbprmBuilder.setJointBounds("root_joint",
self.root_translation_bounds)
self.rbprmBuilder.boundSO3(self.root_rotation_bounds)
self.rbprmBuilder.client.robot.setExtraConfigSpaceBounds(
self.extra_dof_bounds)
def set_rom_filters(self):
"""
Define which ROM must be in collision at all time and with which kind of affordances
By default it set all the roms in used_limbs to be in contact with 'support' affordances
"""
self.rbprmBuilder.setFilter(self.used_limbs)
for limb in self.used_limbs:
self.rbprmBuilder.setAffordanceFilter(limb, ['Support'])
def init_problem(self):
"""
Load the robot, set the bounds and the ROM filters and then
Create a ProblemSolver instance and set the default parameters.
The values of v_max, a_max, mu, size_foot_x and size_foot_y must be defined before calling this method
"""
self.load_rbprm()
self.set_configurations()
self.compute_extra_config_bounds()
self.set_joints_bounds()
self.set_rom_filters()
self.ps = ProblemSolver(self.rbprmBuilder)
# define parameters used by various methods :
if self.v_max >= 0:
self.ps.setParameter("Kinodynamic/velocityBound", self.v_max)
if self.a_max >= 0:
self.ps.setParameter("Kinodynamic/accelerationBound", self.a_max)
if self.size_foot_x > 0:
self.ps.setParameter("DynamicPlanner/sizeFootX", self.size_foot_x)
if self.size_foot_y > 0:
self.ps.setParameter("DynamicPlanner/sizeFootY", self.size_foot_y)
self.ps.setParameter("DynamicPlanner/friction", 0.5)
# sample only configuration with null velocity and acceleration :
self.ps.setParameter("ConfigurationShooter/sampleExtraDOF", False)
def init_viewer(self,
env_name,
env_package="hpp_environments",
reduce_sizes=[0, 0, 0],
visualize_affordances=[]):
"""
Build an instance of hpp-gepetto-viewer from the current problemSolver
:param env_name: name of the urdf describing the environment
:param env_package: name of the package containing this urdf (default to hpp_environments)
:param reduce_sizes: Distance used to reduce the affordances plan toward the center of the plane
(in order to avoid putting contacts closes to the edges of the surface)
:param visualize_affordances: list of affordances type to visualize, default to none
"""
vf = ViewerFactory(self.ps)
self.afftool = AffordanceTool()
self.afftool.setAffordanceConfig('Support', [0.5, 0.03, 0.00005])
self.afftool.loadObstacleModel("package://" + env_package + "/urdf/" +
env_name + ".urdf",
"planning",
vf,
reduceSizes=reduce_sizes)
self.v = vf.createViewer(ghost=True, displayArrows=True)
self.pp = PathPlayer(self.v)
for aff_type in visualize_affordances:
self.afftool.visualiseAffordances(aff_type, self.v,
self.v.color.lightBrown)
def init_planner(self, kinodynamic=True, optimize=True):
"""
Select the rbprm methods, and the kinodynamic ones if required
:param kinodynamic: if True, also select the kinodynamic methods
:param optimize: if True, add randomShortcut path optimizer (or randomShortcutDynamic if kinodynamic is also True)
"""
self.ps.selectConfigurationShooter("RbprmShooter")
self.ps.selectPathValidation("RbprmPathValidation", 0.05)
if kinodynamic:
self.ps.selectSteeringMethod("RBPRMKinodynamic")
self.ps.selectDistance("Kinodynamic")
self.ps.selectPathPlanner("DynamicPlanner")
if optimize:
if kinodynamic:
self.ps.addPathOptimizer("RandomShortcutDynamic")
else:
self.ps.addPathOptimizer("RandomShortcut")
def solve(self):
"""
Solve the path planning problem.
q_init and q_goal must have been defined before calling this method
"""
if len(self.q_init) != self.rbprmBuilder.getConfigSize():
raise ValueError(
"Initial configuration vector do not have the right size")
if len(self.q_goal) != self.rbprmBuilder.getConfigSize():
raise ValueError(
"Goal configuration vector do not have the right size")
self.ps.setInitialConfig(self.q_init)
self.ps.addGoalConfig(self.q_goal)
self.v(self.q_init)
t = self.ps.solve()
print("Guide planning time : ", t)
def display_path(self, path_id=-1, dt=0.1):
"""
Display the path in the viewer, if no path specified display the last one
:param path_id: the Id of the path specified, default to the most recent one
:param dt: discretization step used to display the path (default to 0.1)
"""
if self.pp is not None:
if path_id < 0:
path_id = self.ps.numberPaths() - 1
self.pp.dt = dt
self.pp.displayVelocityPath(path_id)
def play_path(self, path_id=-1, dt=0.01):
"""
play the path in the viewer, if no path specified display the last one
:param path_id: the Id of the path specified, default to the most recent one
:param dt: discretization step used to display the path (default to 0.01)
"""
self.show_rom()
if self.pp is not None:
if path_id < 0:
path_id = self.ps.numberPaths() - 1
self.pp.dt = dt
self.pp(path_id)
def hide_rom(self):
"""
Remove the current robot from the display
"""
self.v.client.gui.setVisibility(self.robot_node_name, "OFF")
def show_rom(self):
"""
Add the current robot to the display
"""
self.v.client.gui.setVisibility(self.robot_node_name, "ON")
@abstractmethod
def run(self):
"""
Must be defined in the child class to run all the methods with the correct arguments.
"""
# example of definition:
"""
self.init_problem()
# define initial and goal position
self.q_init[:2] = [0, 0]
self.q_goal[:2] = [1, 0]
self.init_viewer("multicontact/ground", visualize_affordances=["Support"])
self.init_planner()
self.solve()
self.display_path()
self.play_path()
"""
pass
# We also bound the rotations of the torso. (z, y, x)
rbprmBuilder.boundSO3([-0.1, 0.1, -0.65, 0.65, -0.2, 0.2])
rbprmBuilder.client.basic.robot.setDimensionExtraConfigSpace(extraDof)
rbprmBuilder.client.basic.robot.setExtraConfigSpaceBounds(
[-vMax, vMax, -1, 1, -2, 2, 0, 0, 0, 0, 0, 0])
# Creating an instance of HPP problem solver and the viewer
from hpp.corbaserver.rbprm.problem_solver import ProblemSolver
ps = ProblemSolver(rbprmBuilder)
ps.client.problem.setParameter("aMax", aMax)
ps.client.problem.setParameter("vMax", vMax)
r = Viewer(ps)
from hpp.corbaserver.affordance.affordance import AffordanceTool
afftool = AffordanceTool()
afftool.setAffordanceConfig('Support', [0.5, 0.03, 0.00005])
afftool.loadObstacleModel(packageName, "downSlope", "planning", r)
#r.loadObstacleModel (packageName, "ground", "planning")
afftool.visualiseAffordances('Support', r, [0.25, 0.5, 0.5])
r.addLandmark(r.sceneName, 1)
# Setting initial and goal configurations
q_init = rbprmBuilder.getCurrentConfig()
q_init[3:7] = [0.9659, 0, 0.2588, 0]
q_init[0:3] = [-1.25, 1, 1.7]
r(q_init)
#q_init[3:7] = [0.7071,0,0,0.7071]
#q_init [0:3] = [1, 1, 0.65]
rbprmBuilder.setCurrentConfig(q_init)
q_goal = q_init[::]
r(q_init)
# Set coulour variables for different affordance types
SupportColour = [0.0, 0.95, 0.80]
LeanColour = [0.9, 0.5, 0]
# Import the affordance helper class to extract useful surface
# objects from the environment, and create an instance of affordanceTool
from hpp.corbaserver.affordance.affordance import AffordanceTool
afftool = AffordanceTool()
# Set the affordance configuration. The configuration vector has size 3
# and comprises the error margin, the angle margin for neighbouring triangles
# and the minimum area, in that order.
# If no configuration is set, a default configuration is used.
afftool.setAffordanceConfig('Support', [0.3, 0.3, 0.05])
afftool.setAffordanceConfig('Lean', [0.1, 0.3, 0.05])
# Load obstacle models and visualise affordances. When loading an obstacle,
# the affordance analysis is done automatically.
afftool.loadObstacleModel("hpp-affordance-corba", "darpa", "planning", r)
afftool.visualiseAffordances('Support', r, SupportColour)
# If affordance configuration is changed, the affordance analysis must
# be relaunched.
# First, delete the already created affordance objects (this function also
# deletes them from viewer)
afftool.deleteAffordances(r)
# Next, change the configuration settings for affordance type 'Support'.
# Notice that only the minimum area is changed.
afftool.setAffordanceConfig('Support', [0.3, 0.3, 0.1])
q_init = robot.getCurrentConfig()
q_init[0:3] = [-2, 0, 0.63]
robot.setCurrentConfig(q_init)
r(q_init)
# Set coulour variables for different affordance types
SupportColour = [0.0, 0.95, 0.80]
LeanColour = [0.9, 0.5, 0]
afftool = AffordanceTool()
# Set the affordance configuration. The configuration vector has size 3
# and comprises the error margin, the angle margin for neighbouring triangles
# and the minimum area, in that order.
# If no configuration is set, a default configuration is used.
afftool.setAffordanceConfig("Support", [0.3, 0.3, 0.05])
afftool.setAffordanceConfig("Lean", [0.1, 0.3, 0.05])
# Load obstacle models and visualise affordances. When loading an obstacle,
# the affordance analysis is done automatically.
afftool.loadObstacleModel("hpp-affordance-corba", "darpa", "planning", r)
afftool.visualiseAffordances("Support", r, SupportColour)
# If affordance configuration is changed, the affordance analysis must
# be relaunched.
# First, delete the already created affordance objects (this function also
# deletes them from viewer)
afftool.deleteAffordances(r)
# Next, change the configuration settings for affordance type 'Support'.
# Notice that only the minimum area is changed.
afftool.setAffordanceConfig("Support", [0.3, 0.3, 0.1])
#~ q_init [0:3] = [0, -0.63, 0.6]; rbprmBuilder.setCurrentConfig (q_init); r (q_init)
#~ q_init [3:7] = [ 0.98877108, 0. , 0.14943813, 0. ]
q_goal = q_init [::]
q_goal [3:7] = [ 0.98877108, 0. , 0.14943813, 0. ]
q_goal [0:3] = [1.49, -0.65, 1.25]; r (q_goal)
#~ q_goal [0:3] = [1.2, -0.65, 1.1]; r (q_goal)
#~ ps.addPathOptimizer("GradientBased")
ps.addPathOptimizer("RandomShortcut")
ps.setInitialConfig (q_init)
ps.addGoalConfig (q_goal)
from hpp.corbaserver.affordance.affordance import AffordanceTool
afftool = AffordanceTool ()
afftool.setAffordanceConfig('Support', [0.5, 0.03, 0.00005])
afftool.loadObstacleModel (packageName, "stair_bauzil", "planning", r)
#~ afftool.analyseAll()
afftool.visualiseAffordances('Support', r, [0.25, 0.5, 0.5])
#~ afftool.visualiseAffordances('Lean', r, [0, 0, 0.9])
ps.client.problem.selectConFigurationShooter("RbprmShooter")
ps.client.problem.selectPathValidation("RbprmPathValidation",0.05)
#~ ps.solve ()
t = ps.solve ()
print t;
if isinstance(t, list):
t = t[0]* 3600000 + t[1] * 60000 + t[2] * 1000 + t[3]
f = open('log.txt', 'a')
f.write("path computation " + str(t) + "\n")
r (q_init)
# Set coulour variables for different affordance types
SupportColour = [0.0, 0.95, 0.80]
LeanColour = [0.9, 0.5, 0]
# Import the affordance helper class to extract useful surface
# objects from the environment, and create an instance of affordanceTool
from hpp.corbaserver.affordance.affordance import AffordanceTool
afftool = AffordanceTool ()
# Set the affordance configuration. The configuration vector has size 3
# and comprises the error margin, the angle margin for neighbouring triangles
# and the minimum area, in that order.
# If no configuration is set, a default configuration is used.
afftool.setAffordanceConfig('Support', [0.3, 0.3, 0.05])
afftool.setAffordanceConfig('Lean', [0.1, 0.3, 0.05])
# Load obstacle models and visualise affordances. When loading an obstacle,
# the affordance analysis is done automatically.
afftool.loadObstacleModel ("hpp-affordance-corba", "darpa", "planning", r)
afftool.visualiseAffordances('Support', r, SupportColour)
# If affordance configuration is changed, the affordance analysis must
# be relaunched.
# First, delete the already created affordance objects (this function also
# deletes them from viewer)
afftool.deleteAffordances(r)
# Next, change the configuration settings for affordance type 'Support'.
# Notice that only the minimum area is changed.
afftool.setAffordanceConfig('Support', [0.3, 0.3, 0.1])
q_goal = q_init[::]
#~ q_goal [3:7] = [ 0.98877108, 0. , 0.14943813, 0. ]
q_goal[0:3] = [0.9, 0, 0.50]
r(q_goal)
#~ q_goal [0:3] = [3, -0.82, 6]; r(q_goal)
#~ q_goal [0:3] = [1.2, -0.65, 1.1]; r (q_goal)
#~ ps.addPathOptimizer("GradientBased")
ps.addPathOptimizer("RandomShortcut")
ps.setInitialConfig(q_init)
ps.addGoalConfig(q_goal)
from hpp.corbaserver.affordance.affordance import AffordanceTool
afftool = AffordanceTool()
#~ afftool.setAffordanceConfig('Support', [0.5, 0.03, 0.00005])
afftool.setAffordanceConfig('Lean', [0.5, 0.03, 0.00005])
#~ afftool.loadObstacleModel (packageName, "scale_spidey", "planning", r)
afftool.loadObstacleModel(packageName, "down", "planning", r)
#~ afftool.analyseAll()
afftool.visualiseAffordances('Support', r, [0.25, 0.5, 0.5])
afftool.visualiseAffordances('Lean', r, [0, 0, 0.9])
#~ ps.client.problem.selectConFigurationShooter("RbprmShooter")
#~ ps.client.problem.selectPathValidation("RbprmPathValidation",0.05)
#~ ps.solve ()
t = ps.solve()
print t
if isinstance(t, list):
t = t[0] * 3600000 + t[1] * 60000 + t[2] * 1000 + t[3]
f = open('log.txt', 'a')
