用群体智能让长轴重卡自动防折叠和互撞
AVOID-JACK: Avoidance of Jackknifing for Swarms of Long Heavy Articulated Vehicles
- 纯反应式分布式策略，专为长轴铰接车设计
- 单车99.8%防折叠，双车99.7%无互撞
- 适合物流、采矿等重载车辆自动化场景
本文提出一种新型方法，通过去中心化群体智能避免重型铰接车辆（HAVs）的折叠（jackknifing）及相互碰撞。与传统群体机器人研究不同，这些机器人具有细长结构和复杂运动学，带来独特挑战。尽管该问题在物流自动化、远程采矿、机场行李运输和农业作业中至关重要，但现有文献尚未涉及。为此，我们设计了一种完全基于反应的去中心化群体智能策略，专门用于自动化长轴铰接车辆。该方法优先保障防折叠，同时为互撞规避奠定基础。通过大量仿真验证，单辆HAV实验中，99.8%成功避免折叠，86.7%和83.4%分别达成第一和第二目标；两辆HAV交互时，折叠避免率98.9%，79.4%和65.1%达成目标，且99.7%未发生互撞。
原文摘要 · Abstract (English)
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.
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