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...

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Veröffentlicht in:Advanced intelligent systems Jg. 7; H. 1
Hauptverfasser: Lin‐Yang, Da‐hui, Pastor, Francisco, García‐Cerezo, Alfonso J.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Weinheim John Wiley & Sons, Inc 01.01.2025
Wiley
Schlagworte:
Algorithms
Computational efficiency
Computing costs
Computing time
Configuration management
Efficiency
Energy consumption
Methods
motion plannings
Optimization
optimization‐based methods
Planning
Pontryagin principle
Robots
time‐optimal plannings
Trajectory optimization
Trajectory planning
Waypoints
ISSN:2640-4567, 2640-4567
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Abstract 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. In this article, a time‐optimal trajectory planner for mobile robots using optimization techniques is presented. In the proposed method, multi‐waypoint trajectories in milliseconds are calculated, ensuring fast and efficient performance. Both simulations and real‐world experiments validate its performance, obtaining near‐optimal trajectories with a significant reduction in computation time compared to the benchmark, making it practical for real‐time applications.
AbstractList 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.
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. In this article, a time‐optimal trajectory planner for mobile robots using optimization techniques is presented. In the proposed method, multi‐waypoint trajectories in milliseconds are calculated, ensuring fast and efficient performance. Both simulations and real‐world experiments validate its performance, obtaining near‐optimal trajectories with a significant reduction in computation time compared to the benchmark, making it practical for real‐time applications.
Author Pastor, Francisco
Lin‐Yang, Da‐hui
García‐Cerezo, Alfonso J.
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SubjectTerms Algorithms
Computational efficiency
Computing costs
Computing time
Configuration management
Efficiency
Energy consumption
Methods
motion plannings
Optimization
optimization‐based methods
Planning
Pontryagin principle
Robots
time‐optimal plannings
Trajectory optimization
Trajectory planning
Waypoints
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Title Low Computational Cost for Multiple Waypoints Trajectory Planning: A Time‐Optimal‐Based Approach
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