Topology-Driven Trajectory Optimization for Modelling Controllable Interactions Within Multi-Vehicle Scenario
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866910863674310656 |
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| author | Ma, Changjia Zhao, Yi Gan, Zhongxue Gao, Bingzhao Ding, Wenchao |
| author_facet | Ma, Changjia Zhao, Yi Gan, Zhongxue Gao, Bingzhao Ding, Wenchao |
| contents | Trajectory optimization in multi-vehicle scenarios faces challenges due to its non-linear, non-convex properties and sensitivity to initial values, making interactions between vehicles difficult to control. In this paper, inspired by topological planning, we propose a differentiable local homotopy invariant metric to model the interactions. By incorporating this topological metric as a constraint into multi-vehicle trajectory optimization, our framework is capable of generating multiple interactive trajectories from the same initial values, achieving controllable interactions as well as supporting user-designed interaction patterns. Extensive experiments demonstrate its superior optimality and efficiency over existing methods. We will release open-source code to advance relative research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_05471 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Topology-Driven Trajectory Optimization for Modelling Controllable Interactions Within Multi-Vehicle Scenario Ma, Changjia Zhao, Yi Gan, Zhongxue Gao, Bingzhao Ding, Wenchao Robotics Trajectory optimization in multi-vehicle scenarios faces challenges due to its non-linear, non-convex properties and sensitivity to initial values, making interactions between vehicles difficult to control. In this paper, inspired by topological planning, we propose a differentiable local homotopy invariant metric to model the interactions. By incorporating this topological metric as a constraint into multi-vehicle trajectory optimization, our framework is capable of generating multiple interactive trajectories from the same initial values, achieving controllable interactions as well as supporting user-designed interaction patterns. Extensive experiments demonstrate its superior optimality and efficiency over existing methods. We will release open-source code to advance relative research. |
| title | Topology-Driven Trajectory Optimization for Modelling Controllable Interactions Within Multi-Vehicle Scenario |
| topic | Robotics |
| url | https://arxiv.org/abs/2503.05471 |