Bases: MinimumCurvaturePlanner
FTM shortest path planner. It uses convex optimization (QP) to minimize the path length.
Paper: Heilmeier, A., Wischnewski, A., Hermansdorfer, L., Betz, J., Lienkamp, M., & Lohmann, B. (2020). Minimum curvature trajectory planning and control for an autonomous race car. Vehicle System Dynamics.
Based on: AVS and FTM (2024): TUMFTM/global_racetrajectory_optimization. Available online at: https://github.com/TUMFTM/global_racetrajectory_optimization,
AVS and FTM (2024): TUMFTM/trajectory_planning_helpers. Available online at: https://github.com/TUMFTM/trajectory_planning_helpers
Source code in commonroad_raceline_planner/planner/ftm_planner/ftm_sp_planner.py
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45 | class ShortestPathPlanner(MinimumCurvaturePlanner):
"""
FTM shortest path planner. It uses convex optimization (QP) to minimize the path length.
Paper: Heilmeier, A., Wischnewski, A., Hermansdorfer, L., Betz, J., Lienkamp, M., & Lohmann, B. (2020). Minimum curvature trajectory planning and control for an autonomous race car. Vehicle System Dynamics.
Based on: AVS and FTM (2024): TUMFTM/global_racetrajectory_optimization. Available online at: https://github.com/TUMFTM/global_racetrajectory_optimization,
AVS and FTM (2024): TUMFTM/trajectory_planning_helpers. Available online at: https://github.com/TUMFTM/trajectory_planning_helpers
"""
def __init__(
self, race_track: RaceTrack, config: FTMConfig, logger_level: int = logging.INFO
) -> None:
super().__init__(
race_track=race_track, config=config, logger_level=logger_level
)
# overwrite logger
self._logger = logging.getLogger("FTMPlanner.SP")
self._logger.setLevel(logger_level)
def _optimize(self) -> None:
"""
Call optimization problem
"""
self._alpha_opt = opt_shortest_path(
reftrack=self._preprocessed_dto,
normvectors=self._normvec_normalized_interp,
vehicle_width=self._config.computation_config.optimization_config.opt_shortest_path_config.vehicle_width_opt,
print_debug=self._config.execution_config.debug_config.debug,
)
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