app.nodes["behavior.atlas"]["CompositeAtlas"].config.cask = args.cask
app.load("packages/planner/apps/multi_joint_lqr_control.subgraph.json", prefix="lqr")
# load multi joint lqr control subgraph
lqr_interface = app.nodes["lqr.subgraph"]["interface"]
kinematic_tree = app.nodes["lqr.kinematic_tree"]["KinematicTree"]
lqr_planner = app.nodes["lqr.local_plan"]["MultiJointLqrPlanner"]
app.connect(behavior_interface, "joint_target", lqr_interface, "joint_target")
kinematic_tree.config.kinematic_file = kinematic_file
lqr_planner.config.speed_min = [-1.0] * len(joints)
lqr_planner.config.speed_max = [1.0] * len(joints)
lqr_planner.config.acceleration_min = [-1.0] * len(joints)
lqr_planner.config.acceleration_max = [1.0] * len(joints)
# load hardware subgraph
app.load_module("universal_robots")
arm = app.add("hardware").add(app.registry.isaac.universal_robots.UniversalRobots)
arm.config.robot_ip = "10.32.221.190"
arm.config.control_mode = "joint position"
arm.config.tick_period = '125Hz'
arm.config.kinematic_tree = "lqr.kinematic_tree"
arm.config.tool_digital_out_names = ["valve", "pump"]
arm.config.tool_digital_in_names = ["unknown", "gripper"]
app.connect(arm, "arm_state", lqr_interface, "joint_state")
app.connect(arm, "arm_state", behavior_interface, "joint_state")
app.connect(arm, "io_state", behavior_interface, "io_state")
app.connect(lqr_interface, "joint_command", arm, "arm_command")
app.connect(behavior_interface, "io_command", arm, "io_command")
# visualize IO in sight
def main(args):
app = Application(name="detect_net_inference")
# Load subgraph and get interface node
app.load("packages/detect_net/apps/detect_net_inference.subgraph.json",
prefix="detect_net_inference")
detect_net_inferface = app.nodes["detect_net_inference.subgraph"]\
.components["interface"]
# Load configuration
app.load(args.config)
# Configure detection model
detection_model = app.nodes["detect_net_inference.tensor_r_t_inference"]\
.components["isaac.ml.TensorRTInference"]
if args.detection_model_file_path is not None:
detection_model.config.model_file_path = args.detection_model_file_path
if args.etlt_password is not None:
detection_model.config.etlt_password = args.etlt_password
# Configure detection decoder
decoder = app.nodes["detect_net_inference.detection_decoder"]\
.components["isaac.detect_net.DetectNetDecoder"]
decoder.config.output_scale = [args.rows, args.cols]
if args.confidence_threshold is not None:
decoder.config.confidence_threshold = args.confidence_threshold
if args.nms_threshold is not None:
decoder.config.non_maximum_suppression_threshold = args.nms_threshold
if args.mode == 'cask':
# Load replay subgraph and configure interface node
app.load("packages/record_replay/apps/replay.subgraph.json",
prefix="replay")
replay_interface = app.nodes["replay.interface"].components["output"]
replay_interface.config.cask_directory = args.cask_directory
# Connect the output of the replay subgraph to the detection subgraph
app.connect(replay_interface, "color", detect_net_inferface, "image")
elif args.mode == 'sim':
# Load simulation subgraph and get interface node
app.load("packages/navsim/apps/navsim_training.subgraph.json",\
prefix="simulation")
simulation_interface = app.nodes["simulation.interface"].components[
"output"]
# Connect the output of the simulation with user-specified channel to the detection subgraph
app.connect(simulation_interface, args.image_channel,
detect_net_inferface, "image")
elif args.mode == 'realsense':
app.load_module('realsense')
# Create and configure realsense camera codelet
camera = app.add("camera").add(app.registry.isaac.RealsenseCamera)
camera.config.rows = args.rows
camera.config.cols = args.cols
camera.config.color_framerate = args.fps
camera.config.depth_framerate = args.fps
camera.config.enable_ir_stereo = False
# Connect the output of the camera node to the detection subgraph
app.connect(camera, "color", detect_net_inferface, "image")
elif args.mode == 'v4l':
app.load_module('sensors:v4l2_camera')
# Create and configure V4L camera codelet
camera = app.add("camera").add(app.registry.isaac.V4L2Camera)
camera.config.device_id = 0
camera.config.rows = args.rows
camera.config.cols = args.cols
camera.config.rate_hz = args.fps
# Connect the output of the camera node to the detection subgraph
app.connect(camera, "frame", detect_net_inferface, "image")
elif args.mode == 'image':
app.load_module('message_generators')
# Create feeder node
feeder = app.add("feeder").add(
app.registry.isaac.message_generators.ImageLoader)
feeder.config.color_glob_pattern = args.image_directory
feeder.config.tick_period = "1Hz"
feeder.config.focal_length = [args.focal_length, args.focal_length]
feeder.config.optical_center = [
args.optical_center_rows, args.optical_center_cols
]
feeder.config.distortion_coefficients = [0.01, 0.01, 0.01, 0.01, 0.01]
# Connect the output of the image feeder node to the detection subgraph
app.connect(feeder, "color", detect_net_inferface, "image")
else:
raise ValueError('Not supported mode {}'.format(args.mode))
app.run()
from engine.pyalice import Application
app = Application(name = "mybot")
app.load_module('message_generators')
app.load_module('viewers')
app.load('packages/navsim/apps/navsim_tcp.subgraph.json', 'simulation')
node_view = app.add("viewer")
component_view = node_view.add(app.registry.isaac.viewers.ColorCameraViewer, 'ColorCameraViewer')
node_view = app.add("depth_viewer")
node_view.add(app.registry.isaac.viewers.DepthCameraViewer, 'DepthCameraViewer')
app.connect('simulation.interface/output', 'color', 'viewer/ColorCameraViewer', 'color_listener')
app.connect('simulation.interface/output', 'depth', 'depth_viewer/DepthCameraViewer', 'depth_listener')
app.run()