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

TL;DR

The paper tackles the problem of minimizing the swept volume $\mathbb{V}$ for multi-axle swerve-drive AMRs operating in tight spaces by integrating swept-volume-aware SDF-based trajectory planning with MPC-based independent wheel steering. It frames the problem as joint optimization over trajectory control points $\mathcal{L}$ and MPC inputs $u=[V_x,V_y,\omega]^T$ in $SE(2)$, and solves it via a two-stage MINCO-based planning pipeline followed by MPC tracking, with CUDA-accelerated swept-area estimation and SDF-based obstacle handling. The main contributions include velocity-vector-based wheel-group steering, a four-step planning workflow (A* → LBFGSMINCO smoothing → obstacle/swept-area optimization), and a CUDA-enabled swept-volume estimator, all released as open-source. Experimental results in Gazebo show substantial reductions in excess swept area to $\mathbb{S}_{excess}=23.14\,m^2$, fast planning times ($t=1.17\,s$), and precise tracking ($e_y=0.04\,m$, $e_{\varphi}=0.03^\circ$), highlighting potential impact for safe, efficient industrial AMRs in logistics.

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.

Figures (7)

• Figure 1: This work aims to reduce the minimal swept volume and ensure stable trajectory tracking, enhancing safety in industrial applications..
• Figure 2: The proposed solution uses LiDAR inertial odometry nguyen2024eigen for front-end odometry, with multi-stage back-end planning and MPC to minimize swept volume iteratively.
• Figure 3: Vehicle Parametric Model
• Figure 4: Multi-Axle AMR MPC tracking control.
• Figure 5: Experimental results show the proposed MPC accurately tracks the planned trajectory while minimizing robot travel in LiDAR blind spots.