基于概率安全约束的去中心化多机器人避障新方法
Decentralized Uncertainty-Aware Multi-Agent Collision Avoidance with Model Predictive Path Integral
- 将MPPI与概率互惠避障结合，通过二次锥规划融入安全约束
- 在密集环境仍保持高成功率，仿真中优于ORCA-DD和B-UAVC
- 适合需要独立决策的差速机器人集群，尤其适用于噪声观测场景
去中心化多智能体导航在不确定性下的避障是众多机器人应用中的复杂任务，需同时考虑运动学约束、感知与执行噪声。本文提出一种新方法，将模型预测路径积分（MPPI）与最优互惠避障的概率变体相结合。通过二次锥规划，将概率安全约束直接嵌入MPPI采样过程，使各智能体仅依赖局部噪声观测即可独立运行，同时保证安全。我们在差速机器人上进行大量仿真验证，并与当前领先方法ORCA-DD和B-UAVC对比。结果表明，该方法在高密度环境中仍能实现高成功率，显著优于对比算法。此外，在Gazebo模拟器中的验证进一步证实了其在真实机器人平台上的适用性。源代码见：http://github.com/PathPlanning/MPPI-Collision-Avoidance。
原文摘要 · Abstract (English)
Decentralized multi-agent navigation under uncertainty is a complex task that arises in numerous robotic applications. It requires collision avoidance strategies that account for both kinematic constraints, sensing and action execution noise. In this paper, we propose a novel approach that integrates the Model Predictive Path Integral (MPPI) with a probabilistic adaptation of Optimal Reciprocal Collision Avoidance. Our method ensures safe and efficient multi-agent navigation by incorporating probabilistic safety constraints directly into the MPPI sampling process via a Second-Order Cone Programming formulation. This approach enables agents to operate independently using local noisy observations while maintaining safety guarantees. We validate our algorithm through extensive simulations with differential-drive robots and benchmark it against state-of-the-art methods, including ORCA-DD and B-UAVC. Results demonstrate that our approach outperforms them while achieving high success rates, even in densely populated environments. Additionally, validation in the Gazebo simulator confirms its practical applicability to robotic platforms. A source code is available at http://github.com/PathPlanning/MPPI-Collision-Avoidance.
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