针对长挂车群运动中的折叠与碰撞问题，提出防折叠协同控制框架。
PREVENT-JACK: Context Steering for Swarms of Long Heavy Articulated Vehicles
- 基于六种局部行为融合的稀疏上下文引导策略
- 万次仿真验证：大群体中死锁/活锁影响超27%/31%车辆
- 适用于物流车队、园区接驳等长挂车协同场景
本文研究长挂车（HAVs）群体的集群运动问题。与传统点质量机器人不同，长挂车具有运动学约束、长形结构和关节连接特性，带来独特挑战。为实现局部去中心化协调，提出 Prevent-Jack 框架，引入机器人领域中较少涉及的稀疏上下文引导机制。该方法融合六种局部行为，有效避免折叠与碰撞，代价是可能出现死锁或活锁。实验在最多十节挂车的车辆上进行，通过15,000次仿真评估群体性能。结果表明，大群体和高密度场景下死锁与活锁发生率显著上升，峰值影响比例分别达27%和31%。同时观察到，大群体整体等待时间增加，而小群体则更多表现为规避行为。
原文摘要 · Abstract (English)
In this paper, we aim to extend the traditional point-mass-like robot representation in swarm robotics and instead study a swarm of long Heavy Articulated Vehicles (HAVs). HAVs are kinematically constrained, elongated, and articulated, introducing unique challenges. Local, decentralized coordination of these vehicles is motivated by many real-world applications. Our approach, Prevent-Jack, introduces the sparsely covered context steering framework in robotics. It fuses six local behaviors, providing guarantees against jackknifing and collisions at the cost of potential dead- and livelocks, tested for vehicles with up to ten trailers. We highlight the importance of the Evade Attraction behavior for deadlock prevention using a parameter study, and use 15,000 simulations to evaluate the swarm performance. Our extensive experiments and the results show that both the dead- and livelocks occur more frequently in larger swarms and denser scenarios, affecting a peak average of 27%/31% of vehicles. We observe that larger swarms exhibit increased waiting, while smaller swarms show increased evasion.
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