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

TL;DR

This paper introduces Prevent-Jack, a decentralized control framework for swarms of long, articulated heavy vehicles, ensuring safety and coordination through context steering with extensive simulation validation.

Contribution

It presents a novel context steering approach for HAV swarms, guaranteeing collision avoidance and jackknifing prevention in complex, constrained vehicle formations.

Findings

Dead- and livelocks increase with swarm size and density.

Larger swarms wait more, smaller swarms evade more.

27-31% of vehicles are affected by dead- or livelocks in dense scenarios.

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.