PREVENT-JACK: Context Steering for Swarms of Long Heavy Articulated Vehicles

• URL: http://arxiv.org/abs/2604.21337v1
• Date: Thu, 23 Apr 2026 06:50:00 GMT
• Title: PREVENT-JACK: Context Steering for Swarms of Long Heavy Articulated Vehicles
• Abstract summary: We study a swarm of long Heavy Articulated Vehicles (HAVs)<n>Our approach, Prevent-Jack, introduces the sparsely covered context steering framework in robotics.<n>It fuses six local behaviors, providing guarantees against jackknifing and collisions at the cost of potential dead- and livelocks.
• Score: 0.7699714865575188
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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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