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Copy pathlocal_planner_node.cfg
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executable file
·33 lines (27 loc) · 2.41 KB
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#!/usr/bin/env python
PACKAGE = "avoidance"
from dynamic_reconfigure.parameter_generator_catkin import *
gen = ParameterGenerator()
# local_planner
gen.add("max_sensor_range_", double_t, 0, "Data points farther away will be discarded", 15.0, 0, 40)
gen.add("min_sensor_range_", double_t, 0, "Discard points closer than that", 0.2, 0, 10)
gen.add("pitch_cost_param_", double_t, 0, "Cost function weight for goal oriented behavior", 50.0, 0, 100.0)
gen.add("yaw_cost_param_", double_t, 0, "Cost function weight for constant heading", 3.0, 0, 20.0)
gen.add("velocity_cost_param_", double_t, 0, "Cost function weight for path smoothness", 3000, 0.0, 50000.0)
gen.add("obstacle_cost_param_", double_t, 0,"Approximate distance from obstacles (m) when the obstacle distance term dominates the cost function", 5.0, 0.0, 30.0)
gen.add("tree_heuristic_weight_", double_t, 0, "Weight for the tree heuristic cost", 35.0, 0.0, 50.0)
gen.add("tree_step_size_s_", double_t, 0, "Tree time step size in seconds", 0.05, 0.01, 5.0)
gen.add("goal_z_param", double_t, 0, "Height of the goal position", 3.5, -20.0, 20.0)
gen.add("timeout_startup_", double_t, 0, "After this timeout the companion status is MAV_STATE_CRITICAL", 5, 0, 60)
gen.add("timeout_critical_", double_t, 0, "After this timeout the companion status is MAV_STATE_CRITICAL", 0.5, 0, 10)
gen.add("timeout_termination_", double_t, 0, "After this timeout the companion status is MAV_STATE_FLIGHT_TERMINATION", 15, 0, 1000)
gen.add("max_point_age_s_", double_t, 0, "maximum age of a remembered data point", 20, 0, 500)
gen.add("min_num_points_per_cell_", int_t, 0, "minimum number of points in one area to be kept, if lower they are discarded as noise", 1, 1, 500)
gen.add("smoothing_speed_xy_", double_t, 0, "response speed of the smoothing system in xy (set to 0 to disable)", 10, 0, 30)
gen.add("smoothing_speed_z_", double_t, 0, "response speed of the smoothing system in z (set to 0 to disable)", 3, 0, 30)
gen.add("smoothing_margin_degrees_", double_t, 0, "smoothing radius for obstacle cost in cost histogram", 40, 0, 90)
# star_planner
gen.add("children_per_node_", int_t, 0, "Branching factor of the search tree", 8, 0, 100)
gen.add("n_expanded_nodes_", int_t, 0, "Number of nodes expanded in complete tree", 40, 0, 200)
gen.add("tree_node_duration_", double_t, 0, "Distance in seconds between nodes", 0.5, 0.01, 2)
exit(gen.generate(PACKAGE, "avoidance", "LocalPlannerNode"))