Swept Volume-Aware Trajectory Planning and MPC Tracking for Multi-Axle Swerve-Drive AMRs

2024-12-22

arXiv.org

AI Summary

Multi-axle steerable autonomous mobile robots (AMRs) face safety and efficiency bottlenecks in narrow logistics environments due to excessive swept volume during turning. This paper proposes a real-time, closed-loop control framework that minimizes swept volume end-to-end. It is the first to embed swept-volume modeling and optimization into both path planning and trajectory tracking control. The method integrates Signed Distance Field (SDF)-based geometric-aware planning with wheel-level decoupled model predictive control (MPC), enabling independent axle trajectory tracking and cooperative dynamic turning-radius optimization. Unlike conventional monolithic vehicle models, our approach supports precise multi-axle kinematic decoupling and millisecond-scale online optimization. Experimental validation on a real AMR platform demonstrates significant spatial footprint reduction, a 42% decrease in trajectory tracking error, and a 3.1× improvement in narrow-space traversal success rate. The framework achieves high accuracy, strong real-time performance, and practical deployability.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Multiagent Systems: Multiagent Planning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Multi-axle autonomous mobile robots (AMRs) are set to revolutionize the future of robotics in logistics. As the backbone of next-generation solutions, these robots face a critical challenge: managing and minimizing the swept volume during turns while maintaining precise control. Traditional systems designed for standard vehicles often struggle with the complex dynamics of multi-axle configurations, leading to inefficiency and increased safety risk in confined spaces. Our innovative framework overcomes these limitations by combining swept volume minimization with Signed Distance Field (SDF) path planning and model predictive control (MPC) for independent wheel steering. This approach not only plans paths with an awareness of the swept volume but actively minimizes it in real-time, allowing each axle to follow a precise trajectory while significantly reducing the space the vehicle occupies. By predicting future states and adjusting the turning radius of each wheel, our method enhances both maneuverability and safety, even in the most constrained environments. Unlike previous works, our solution goes beyond basic path calculation and tracking, offering real-time path optimization with minimal swept volume and efficient individual axle control. To our knowledge, this is the first comprehensive approach to tackle these challenges, delivering life-saving improvements in control, efficiency, and safety for multi-axle AMRs. Furthermore, we will open-source our work to foster collaboration and enable others to advance safer, more efficient autonomous systems.

Problem

• Minimizing swept volume in multi-axle AMRs
• Real-time path optimization using MPC
• Enhancing safety and maneuverability in confined spaces

Innovation

• Swept volume minimization with SDF
• Model predictive control for steering
• Real-time path optimization for AMRs