Low Computational Cost for Multiple Waypoints Trajectory Planning: A Time‐Optimal‐Based Approach
In the field of mobile robots, achieving minimum time in executing trajectories is crucial for applications like delivery, inspection, and search and rescue. In this article, a novel time‐optimal planner based on optimization methods is introduced. Despite the high computational cost associated with...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-01-01
|
| Series: | Advanced Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/aisy.202400363 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832592603385692160 |
|---|---|
| author | Da‐hui Lin‐Yang Francisco Pastor Alfonso J. García‐Cerezo |
| author_facet | Da‐hui Lin‐Yang Francisco Pastor Alfonso J. García‐Cerezo |
| author_sort | Da‐hui Lin‐Yang |
| collection | DOAJ |
| description | In the field of mobile robots, achieving minimum time in executing trajectories is crucial for applications like delivery, inspection, and search and rescue. In this article, a novel time‐optimal planner based on optimization methods is introduced. Despite the high computational cost associated with such methods, the solution calculates time‐optimal multi‐waypoint trajectories, achieving results in the order of milliseconds. The proposed method formulates a time‐optimal trajectory using the Pontryagin's maximum principle as a policy. By utilizing a point mass model, the planner generates trajectories that are adaptable to different robot models. The approach incorporates a definition of a search space to guarantee convergence while considering the system limits. Simulation and real‐world experiments are performed to validate the feasibility of our method with different configurations. Simulation results compared to a benchmark method demonstrate our approach's superior performance in terms of computational time, achieving near‐optimal solutions. In addition, in the real‐world experiments, the integration of the method into practical applications is validated. |
| format | Article |
| id | doaj-art-db5a2e9fa010441194f3dcf483a9fa75 |
| institution | Kabale University |
| issn | 2640-4567 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj-art-db5a2e9fa010441194f3dcf483a9fa752025-01-21T07:26:27ZengWileyAdvanced Intelligent Systems2640-45672025-01-0171n/an/a10.1002/aisy.202400363Low Computational Cost for Multiple Waypoints Trajectory Planning: A Time‐Optimal‐Based ApproachDa‐hui Lin‐Yang0Francisco Pastor1Alfonso J. García‐Cerezo2Robotics and Mechatronics Lab, Institute for Mechatronics Engineering & Cyber‐physical Systems (IMECH.UMA) Universidad de Málaga 29071 Málaga SpainRobotics and Mechatronics Lab, Institute for Mechatronics Engineering & Cyber‐physical Systems (IMECH.UMA) Universidad de Málaga 29071 Málaga SpainRobotics and Mechatronics Lab, Institute for Mechatronics Engineering & Cyber‐physical Systems (IMECH.UMA) Universidad de Málaga 29071 Málaga SpainIn the field of mobile robots, achieving minimum time in executing trajectories is crucial for applications like delivery, inspection, and search and rescue. In this article, a novel time‐optimal planner based on optimization methods is introduced. Despite the high computational cost associated with such methods, the solution calculates time‐optimal multi‐waypoint trajectories, achieving results in the order of milliseconds. The proposed method formulates a time‐optimal trajectory using the Pontryagin's maximum principle as a policy. By utilizing a point mass model, the planner generates trajectories that are adaptable to different robot models. The approach incorporates a definition of a search space to guarantee convergence while considering the system limits. Simulation and real‐world experiments are performed to validate the feasibility of our method with different configurations. Simulation results compared to a benchmark method demonstrate our approach's superior performance in terms of computational time, achieving near‐optimal solutions. In addition, in the real‐world experiments, the integration of the method into practical applications is validated.https://doi.org/10.1002/aisy.202400363motion planningsoptimization‐based methodstime‐optimal plannings |
| spellingShingle | Da‐hui Lin‐Yang Francisco Pastor Alfonso J. García‐Cerezo Low Computational Cost for Multiple Waypoints Trajectory Planning: A Time‐Optimal‐Based Approach Advanced Intelligent Systems motion plannings optimization‐based methods time‐optimal plannings |
| title | Low Computational Cost for Multiple Waypoints Trajectory Planning: A Time‐Optimal‐Based Approach |
| title_full | Low Computational Cost for Multiple Waypoints Trajectory Planning: A Time‐Optimal‐Based Approach |
| title_fullStr | Low Computational Cost for Multiple Waypoints Trajectory Planning: A Time‐Optimal‐Based Approach |
| title_full_unstemmed | Low Computational Cost for Multiple Waypoints Trajectory Planning: A Time‐Optimal‐Based Approach |
| title_short | Low Computational Cost for Multiple Waypoints Trajectory Planning: A Time‐Optimal‐Based Approach |
| title_sort | low computational cost for multiple waypoints trajectory planning a time optimal based approach |
| topic | motion plannings optimization‐based methods time‐optimal plannings |
| url | https://doi.org/10.1002/aisy.202400363 |
| work_keys_str_mv | AT dahuilinyang lowcomputationalcostformultiplewaypointstrajectoryplanningatimeoptimalbasedapproach AT franciscopastor lowcomputationalcostformultiplewaypointstrajectoryplanningatimeoptimalbasedapproach AT alfonsojgarciacerezo lowcomputationalcostformultiplewaypointstrajectoryplanningatimeoptimalbasedapproach |