KRRF: Kinodynamic Rapidly-exploring Random Forest algorithm for multi-goal motion planning
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866910935263739904 |
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| author | Ježek, Petr Minařík, Michal Vonásek, Vojtěch Pěnička, Robert |
| author_facet | Ježek, Petr Minařík, Michal Vonásek, Vojtěch Pěnička, Robert |
| contents | The problem of kinodynamic multi-goal motion planning is to find a trajectory over multiple target locations with an apriori unknown sequence of visits. The objective is to minimize the cost of the trajectory planned in a cluttered environment for a robot with a kinodynamic motion model. This problem has yet to be efficiently solved as it combines two NP-hard problems, the Traveling Salesman Problem~(TSP) and the kinodynamic motion planning problem. We propose a novel approximate method called Kinodynamic Rapidly-exploring Random Forest~(KRRF) to find a collision-free multi-goal trajectory that satisfies the motion constraints of the robot. KRRF simultaneously grows kinodynamic trees from all targets towards all other targets while using the other trees as a heuristic to boost the growth. Once the target-to-target trajectories are planned, their cost is used to solve the TSP to find the sequence of targets. The final multi-goal trajectory satisfying kinodynamic constraints is planned by guiding the RRT-based planner along the target-to-target trajectories in the TSP sequence. Compared with existing approaches, KRRF provides shorter target-to-target trajectories and final multi-goal trajectories with $1.1-2$ times lower costs while being computationally faster in most test cases. The method will be published as an open-source library. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_06126 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | KRRF: Kinodynamic Rapidly-exploring Random Forest algorithm for multi-goal motion planning Ježek, Petr Minařík, Michal Vonásek, Vojtěch Pěnička, Robert Robotics The problem of kinodynamic multi-goal motion planning is to find a trajectory over multiple target locations with an apriori unknown sequence of visits. The objective is to minimize the cost of the trajectory planned in a cluttered environment for a robot with a kinodynamic motion model. This problem has yet to be efficiently solved as it combines two NP-hard problems, the Traveling Salesman Problem~(TSP) and the kinodynamic motion planning problem. We propose a novel approximate method called Kinodynamic Rapidly-exploring Random Forest~(KRRF) to find a collision-free multi-goal trajectory that satisfies the motion constraints of the robot. KRRF simultaneously grows kinodynamic trees from all targets towards all other targets while using the other trees as a heuristic to boost the growth. Once the target-to-target trajectories are planned, their cost is used to solve the TSP to find the sequence of targets. The final multi-goal trajectory satisfying kinodynamic constraints is planned by guiding the RRT-based planner along the target-to-target trajectories in the TSP sequence. Compared with existing approaches, KRRF provides shorter target-to-target trajectories and final multi-goal trajectories with $1.1-2$ times lower costs while being computationally faster in most test cases. The method will be published as an open-source library. |
| title | KRRF: Kinodynamic Rapidly-exploring Random Forest algorithm for multi-goal motion planning |
| topic | Robotics |
| url | https://arxiv.org/abs/2505.06126 |