arXiv cs.AI· Pedro Antonio Alarcon Granadeno, Jane Cleland-Huang·· 10 小时前AI 评分27
DOBB：面向状态不确定与异构动力学下多智能体避撞的动态有向包围盒
DOBB: Dynamic Oriented Bounding Boxes for Multi-Agent Collision Avoidance under State Uncertainty and Heterogeneous Dynamics
AI 导读
DOBB 用在线几何表示把当前状态估计、不确定性与智能体响应特性映射为随方向变化的动态安全裕度，在四个评测环境中实现最低的平均总失败率 2.6%。在驱动动力学与状态估计不确定性的分布外偏移下，基于碰撞的失败率在每个测试条件下均保持不高于 2.3%。作者将鲁棒性归因于安全裕度的几何抽象，使其与训练中遇到的具体不确定性和驱动机制耦合更弱。
正文 · 原文
Abstract:Learning-based decentralized collision avoidance must remain reliable under state-estimation uncertainty and heterogeneous agent dynamics. Existing methods typically address these factors through training-domain variation or by exposing uncertainty and capability estimates to the policy at runtime. However, both leave the policy to infer how these quantities should alter the physical separation required for safe interaction, which can couple collision-avoidance behavior to relationships learned from the training distribution. We introduce the Dynamic Oriented Bounding Box (DOBB), an online geometric representation that maps the current state estimate, uncertainty, and agent-specific response characteristics into a dynamic, direction-dependent safety margin. We derive geometric features that jointly characterize the relative configuration and severity of interactions between agents' DOBBs, along with a reward function that preserves the physical interpretation of those interactions. Across four evaluation environments, DOBB achieves the lowest mean total failure rate of 2.6%. This performance persists under out-of-distribution shifts in actuation dynamics and state-estimation uncertainty, with collision-based failure remaining at or below 2.3% in every tested condition. We attribute this robustness to the geometric abstraction of the safety margin, which is less coupled to the specific uncertainty and actuation regimes encountered during training.
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.13142 [cs.RO] |
| (or arXiv:2611.13142v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2611.13142 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Pedro Antonio Alarcon Granadeno [view email]
[v1]
Mon, 9 Nov 2026 19:23:34 UTC (293 KB)
来源：arXiv cs.AI · arxiv.org