PREVENT-JACK：群组中长重载 articulated 车辆的上下文引导
PREVENT-JACK: Context Steering for Swarms of Long Heavy Articulated Vehicles
AI总结：
本文提出PREVENT-JACK方法，通过融合六个局部行为，解决长重载 articulated 车辆群组的死锁和碰撞问题，通过15000次模拟验证了大群组和密集场景下的性能影响。
摘要：
本文旨在扩展传统点质点机器人表示，在群体机器人中研究长重载 articulated 车辆群组。HAVs具有运动学约束、长且可折叠的特性，带来独特挑战。我们的方法PREVENT-JACK引入了稀疏覆盖的上下文引导框架，融合六个局部行为，以防止 jackknifing 和碰撞，尽管可能产生死锁和活锁。该方法在最多十节车厢的车辆上进行了测试。我们通过参数研究强调了Evade Attraction行为对死锁预防的重要性，并使用15000次模拟评估群组性能。我们的实验和结果表明，死锁和活锁在较大群组和密集场景中更频繁发生，影响峰值平均27%/31%的车辆。我们观察到较大群组表现出增加的等待，而较小群组则表现出增加的规避。
英文摘要：
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.
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。
