Search papers, labs, and topics across Lattice.
This paper introduces Prevent-Jack, a decentralized control framework for swarms of Heavy Articulated Vehicles (HAVs) that guarantees jackknife and collision avoidance. The framework combines six local behaviors within a sparsely covered context steering approach. Experiments with up to ten trailers and 15,000 simulations reveal that deadlocks and livelocks increase with swarm size and density, affecting up to 31% of vehicles.
Swarms of long, articulated vehicles face surprising deadlock challenges, with up to 31% of vehicles immobilized in dense scenarios despite collision avoidance guarantees.
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.
