PREVENT-JACK: Context Steering for Swarms of Long Heavy Articulated Vehicles
For researchers in swarm robotics and autonomous vehicle coordination, this work addresses the underexplored problem of controlling articulated vehicles in swarms, but the results are incremental, showing limitations rather than breakthroughs.
This paper extends swarm robotics to long Heavy Articulated Vehicles (HAVs) by introducing Prevent-Jack, a context steering framework that fuses six local behaviors to prevent jackknifing and collisions. In simulations with up to 10 trailers, deadlocks and livelocks affected a peak average of 27%/31% of vehicles, occurring more frequently in larger swarms and denser scenarios.
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.