AVOID-JACK: Avoidance of Jackknifing for Swarms of Long Heavy Articulated Vehicles
🤖 AI Summary
Heavy articulated vehicles (HAVs) in platooning operations are prone to jackknifing and inter-vehicle collisions. To address this, we propose a decentralized, reactive swarm intelligence control strategy. Our approach is the first to extend the swarm intelligence paradigm to HAV platoons subject to complex nonlinear kinematic constraints—overcoming the limitation of conventional swarm robotics that rely on simplified motion models. By integrating high-fidelity vehicle kinematic modeling with local sensory feedback, each vehicle achieves real-time, neighbor-aware cooperative collision avoidance. Experimental results demonstrate: 99.8% jackknife avoidance for single vehicles; 98.9% jackknife avoidance and 99.7% collision avoidance in two-vehicle interaction scenarios; and robust, scalable coordination in multi-vehicle platoons. This work establishes a novel, scalable, and robust framework for autonomous HAV swarms in logistics, mining, and airport operations.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Avoiding jackknifing in heavy articulated vehicles using swarm intelligence
Preventing mutual collisions in elongated robotic swarms with complex kinematics
Developing decentralized control for logistics automation and mining applications
Innovation
Methods, ideas, or system contributions that make the work stand out.
Decentralized swarm intelligence for heavy vehicles
Reaction-based strategy prevents jackknifing collisions
Tailored automation for elongated articulated vehicles
🔎 Similar Papers
No similar papers found.
