FG-PE：多机器人追逃问题的因子图方法
FG-PE: Factor-graph Approach for Multi-robot Pursuit-Evasion
AI总结：
本文提出一种基于因子图的多机器人追逃方法，通过协同估计与规划提升追捕精度和速度，并在通信丢包下保持稳健，实验验证其性能优于传统方法。
摘要：
随着机器人在日常生活中的日益普及，越来越需要为机器人提供稳健的协作协议，以有效处理更复杂和动态的问题。本文提出了一种新颖的、基于因子图的方法来解决追逃问题，使多个追捕者能够协同工作，对逃逸者进行准确的估计、规划和跟踪。该场景假设存在多个追捕者且只有一个逃逸者。与现有技术相比，所提出的方法显著提高了对逃逸者估计和跟踪的准确性，使追捕者能够在最短的时间和距离内捕获逃逸者。除了这些主要目标外，所提出的方法还能有效最小化不确定性，即使在通信问题导致部分消息被丢弃或丢失的情况下，仍能保持稳健性。通过一系列全面的实验，本文证明所提出的算法在多个关键性能指标上（如捕获逃逸者所需的时间和追捕者行进的平均距离）始终优于传统的追逃方法。此外，所提出的方法还在真实世界的硬件实验中进行了测试，进一步验证了其有效性和适用性。
英文摘要：
With the increasing use of robots in daily life, there is a growing need to provide robust collaboration protocols for robots to tackle more complicated and dynamic problems effectively. This paper presents a novel, factor graph-based approach to address the pursuit-evasion problem, enabling accurate estimation, planning, and tracking of an evader by multiple pursuers working together. It is assumed that there are multiple pursuers and only one evader in this scenario. The proposed method significantly improves the accuracy of evader estimation and tracking, allowing pursuers to capture the evader in the shortest possible time and distance compared to existing techniques. In addition to these primary objectives, the proposed approach effectively minimizes uncertainty while remaining robust, even when communication issues lead to some messages being dropped or lost. Through a series of comprehensive experiments, this paper demonstrates that the proposed algorithm consistently outperforms traditional pursuit-evasion methods across several key performance metrics, such as the time required to capture the evader and the average distance traveled by the pursuers. Additionally, the proposed method is tested in real-world hardware experiments, further validating its effectiveness and applicability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
