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

2026-04-23

AI Summary

This study addresses the challenges of jackknifing, collisions, and deadlock/livelock that commonly arise in decentralized coordination of long, heavy articulated vehicles (HAVs). To mitigate these issues, the authors propose PREVENT-JACK, a novel approach that introduces a context-guided mechanism into HAV swarm control. The method establishes a sparsely covered local behavioral framework integrating six distinct rules, including a specially designed Evade Attraction behavior to alleviate deadlocks, and supports complex configurations with up to ten trailers. Extensive simulations involving 15,000 trials demonstrate that in high-density, large-group scenarios, up to 27% of vehicles experience deadlock and 31% encounter livelock. Furthermore, smaller groups tend to actively avoid conflicts, whereas larger groups exhibit significantly increased waiting times.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Coordination and Collaboration
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• Systems and Infrastructure for Web, Mobile and WoT: Decentralized Web and Fediverse systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Heavy Articulated Vehicles
• swarm robotics
• deadlock
• livelock
• jackknifing

Innovation

• Heavy Articulated Vehicles
• context steering
• jackknifing prevention
• decentralized coordination
• deadlock avoidance