A Fairness-Oriented Control Framework for Safety-Critical Multi-Robot Systems: Alternative Authority Control
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910606863368192 |
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| author | Shi, Lei Liu, Qichao Zhou, Cheng Li, Xiong |
| author_facet | Shi, Lei Liu, Qichao Zhou, Cheng Li, Xiong |
| contents | This paper proposes a fair control framework for multi-robot systems, which integrates the newly introduced Alternative Authority Control (AAC) and Flexible Control Barrier Function (F-CBF). Control authority refers to a single robot which can plan its trajectory while considering others as moving obstacles, meaning the other robots do not have authority to plan their own paths. The AAC method dynamically distributes the control authority, enabling fair and coordinated movement across the system. This approach significantly improves computational efficiency, scalability, and robustness in complex environments. The proposed F-CBF extends traditional CBFs by incorporating obstacle shape, velocity, and orientation. F-CBF enhances safety by accurate dynamic obstacle avoidance. The framework is validated through simulations in multi-robot scenarios, demonstrating its safety, robustness and computational efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_10749 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Fairness-Oriented Control Framework for Safety-Critical Multi-Robot Systems: Alternative Authority Control Shi, Lei Liu, Qichao Zhou, Cheng Li, Xiong Robotics This paper proposes a fair control framework for multi-robot systems, which integrates the newly introduced Alternative Authority Control (AAC) and Flexible Control Barrier Function (F-CBF). Control authority refers to a single robot which can plan its trajectory while considering others as moving obstacles, meaning the other robots do not have authority to plan their own paths. The AAC method dynamically distributes the control authority, enabling fair and coordinated movement across the system. This approach significantly improves computational efficiency, scalability, and robustness in complex environments. The proposed F-CBF extends traditional CBFs by incorporating obstacle shape, velocity, and orientation. F-CBF enhances safety by accurate dynamic obstacle avoidance. The framework is validated through simulations in multi-robot scenarios, demonstrating its safety, robustness and computational efficiency. |
| title | A Fairness-Oriented Control Framework for Safety-Critical Multi-Robot Systems: Alternative Authority Control |
| topic | Robotics |
| url | https://arxiv.org/abs/2409.10749 |