State Lattice Planning

This script is a path planning code with state lattice planning.

This code uses the model predictive trajectory generator to solve boundary problem.

Uniform polar sampling

Code Link

PathPlanning.StateLatticePlanner.state_lattice_planner.calc_uniform_polar_states(nxy, nh, d, a_min, a_max, p_min, p_max)

Parameters:

• nxy – number of position sampling

• nh – number of heading sampleing

• d – distance of terminal state

• a_min – position sampling min angle

• a_max – position sampling max angle

• p_min – heading sampling min angle

• p_max – heading sampling max angle

Biased polar sampling

Code Link

PathPlanning.StateLatticePlanner.state_lattice_planner.calc_biased_polar_states(goal_angle, ns, nxy, nh, d, a_min, a_max, p_min, p_max)

calc biased state

Parameters:

• goal_angle – goal orientation for biased sampling

• ns – number of biased sampling

• nxy – number of position sampling

• nxy – number of position sampling

• nh – number of heading sampleing

• d – distance of terminal state

• a_min – position sampling min angle

• a_max – position sampling max angle

• p_min – heading sampling min angle

• p_max – heading sampling max angle

Returns:

states list

Lane sampling

Code Link

PathPlanning.StateLatticePlanner.state_lattice_planner.calc_lane_states(l_center, l_heading, l_width, v_width, d, nxy)

calc lane states

Parameters:

• l_center – lane lateral position

• l_heading – lane heading

• l_width – lane width

• v_width – vehicle width

• d – longitudinal position

• nxy – sampling number

Returns:

state list

Reference

• Optimal rough terrain trajectory generation for wheeled mobile robots

• State Space Sampling of Feasible Motions for High-Performance Mobile Robot Navigation in Complex Environments