AVOID-JACK: 避免长重型关节车辆群的翻车
AVOID-JACK: Avoidance of Jackknifing for Swarms of Long Heavy Articulated Vehicles
AI总结：
本文提出一种基于反应的分布式 swarm 智能策略，用于避免长重型关节车辆的翻车和相互碰撞，通过模拟实验验证了其有效性。
摘要：
本文提出了一种新的方法，利用去中心化群体智能来避免重型关节车辆（HAVs）的翻车和相互碰撞。与典型的群体机器人研究不同，我们的机器人是长形的，具有复杂的运动学，带来了独特的挑战。尽管这个问题与现实应用如物流自动化、远程采矿、机场行李运输和农业操作相关，但现有文献中尚未解决。为解决这一新的群体机器人问题，我们提出了一种纯反应式的、去中心化的群体智能策略，专门用于自动化长形关节车辆。本文的方法优先考虑翻车避免，并建立了相互碰撞避免的基础。我们通过广泛的模拟实验验证了我们的方法，并对其性能进行了全面分析。对于单个 HAV 的实验，我们观察到 99.8% 的翻车被成功避免，86.7% 和 83.4% 的车辆分别达到第一个和第二个目标。对于两个 HAV 交互的实验，我们观察到 98.9%、79.4% 和 65.1%，而 99.7% 的 HAV 不经历相互碰撞。
英文摘要：
This paper presents a novel approach to avoiding jackknifing and mutual collisions in Heavy Articulated Vehicles (HAVs) by leveraging decentralized swarm intelligence. In contrast to typical swarm robotics research, our robots are elongated and exhibit complex kinematics, introducing unique challenges. Despite its relevance to real-world applications such as logistics automation, remote mining, airport baggage transport, and agricultural operations, this problem has not been addressed in the existing literature. To tackle this new class of swarm robotics problems, we propose a purely reaction-based, decentralized swarm intelligence strategy tailored to automate elongated, articulated vehicles. The method presented in this paper prioritizes jackknifing avoidance and establishes a foundation for mutual collision avoidance. We validate our approach through extensive simulation experiments and provide a comprehensive analysis of its performance. For the experiments with a single HAV, we observe that for 99.8% jackknifing was successfully avoided and that 86.7% and 83.4% reach their first and second goals, respectively. With two HAVs interacting, we observe 98.9%, 79.4%, and 65.1%, respectively, while 99.7% of the HAVs do not experience mutual collisions.
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。
