用模糊逻辑提升机器人避障能力，更适应动态环境。
Improved Obstacle Avoidance for Autonomous Robots with ORCA-FLC
- 用模糊逻辑控制器优化ORCA算法，增强对不确定性的处理
- 在速度超过阈值时，碰撞次数比ORCA减少
- 适合需要实时避障的多机器人系统应用
障碍物避让使自主机器人能在动态复杂环境中安全高效运行，降低碰撞风险。尽管已有多种避障算法如动态窗口法（DWA）、时间弹性带（TEB）和互惠速度障碍（RVO），但存在权重固定、计算开销大或对多智能体动态障碍适应性差等问题。最优互惠避碰（ORCA）改进了RVO，提供更平滑轨迹和更强的避碰保证。本文提出ORCA-FL，利用模糊逻辑控制器（FLC）更好处理路径规划中的不确定性与不精确性。大量多智能体实验表明，当机器人速度超过某阈值时，ORCA-FL可有效减少碰撞次数。此外，还提出一种基于模糊Q强化学习（FQL）的算法，用于优化和调参FLC。
原文摘要 · Abstract (English)
Obstacle avoidance enables autonomous agents and robots to operate safely and efficiently in dynamic and complex environments, reducing the risk of collisions and damage. For a robot or autonomous system to successfully navigate through obstacles, it must be able to detect such obstacles. While numerous collision avoidance algorithms like the dynamic window approach (DWA), timed elastic bands (TEB), and reciprocal velocity obstacles (RVO) have been proposed, they may lead to suboptimal paths due to fixed weights, be computationally expensive, or have limited adaptability to dynamic obstacles in multi-agent environments. Optimal reciprocal collision avoidance (ORCA), which improves on RVO, provides smoother trajectories and stronger collision avoidance guarantees. We propose ORCA-FL to improve on ORCA by using fuzzy logic controllers (FLCs) to better handle uncertainty and imprecision for obstacle avoidance in path planning. Numerous multi-agent experiments are conducted and it is shown that ORCA-FL can outperform ORCA in reducing the number of collision if the agent has a velocity that exceeds a certain threshold. In addition, a proposed algorithm for improving ORCA-FL using fuzzy Q reinforcement learning (FQL) is detailed for optimizing and tuning FLCs.
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