AVOID-JACK: Avoidance of Jackknifing for Swarms of Long Heavy Articulated Vehicles

TL;DR

This paper introduces a decentralized swarm intelligence method to prevent jackknifing and collisions in long, articulated heavy vehicles, validated through extensive simulations with high success rates.

Contribution

It presents a novel reaction-based, decentralized approach specifically designed for elongated articulated vehicles, addressing a gap in swarm robotics literature.

Findings

99.8% jackknifing avoided in single HAV scenarios

86.7% and 83.4% reach their goals in single HAV experiments

98.9% of HAVs avoid mutual collisions in multi-vehicle scenarios

Abstract

This paper presents a novel approach to avoiding jackknifing and mutual collisions in Heavy Articulated Vehicles (HAVs) by leveraging decentralized swarm intelligence. In contrast to typical swarm robotics research, our robots are elongated and exhibit complex kinematics, introducing unique challenges. Despite its relevance to real-world applications such as logistics automation, remote mining, airport baggage transport, and agricultural operations, this problem has not been addressed in the existing literature. To tackle this new class of swarm robotics problems, we propose a purely reaction-based, decentralized swarm intelligence strategy tailored to automate elongated, articulated vehicles. The method presented in this paper prioritizes jackknifing avoidance and establishes a foundation for mutual collision avoidance. We validate our approach through extensive simulation experiments and provide a comprehensive analysis of its performance. For the experiments with a single HAV, we observe that for 99.8% jackknifing was successfully avoided and that 86.7% and 83.4% reach their first and second goals, respectively. With two HAVs interacting, we observe 98.9%, 79.4%, and 65.1%, respectively, while 99.7% of the HAVs do not experience mutual collisions.