Swept Volume-Aware Trajectory Planning and MPC Tracking for Multi-Axle Swerve-Drive AMRs (2412.16875v2)

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.

• The paper proposes a swept volume-aware trajectory planning framework using Signed Distance Fields (SDF) and Model Predictive Control (MPC) to enhance maneuverability for multi-axle swerve-drive AMRs.
• The method significantly reduces excess swept volume to 23.14 m² and maintains precise tracking with minimal errors (±0.04 m lateral, ±0.03° heading).
• A CUDA-accelerated implementation enables real-time trajectory planning in 1.17 seconds, demonstrating practical suitability for dynamic environments.

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

The paper, "Swept Volume-Aware Trajectory Planning and MPC Tracking for Multi-Axle Swerve-Drive AMRs," addresses a significant challenge in the field of autonomous mobile robots (AMRs): optimizing the trajectory of multi-axle configurations to minimize the swept volume and ensure maneuverability in constrained environments. Multi-axle AMRs, pivotal in logistics and industrial applications, often face limitations in traditional path planning and control methods, which are primarily designed for smaller vehicles. The authors propose a comprehensive framework that combines swept volume minimization with advanced model predictive control (MPC), representing a nuanced approach to handling these sophisticated robotic systems.

Contribution and Technical Approach

The paper details an integrated solution for trajectory planning that accounts for swept volume, utilizing a Signed Distance Field (SDF) methodology. The authors emphasize the significance of real-time trajectory optimization, facilitating each axle in a multi-axle vehicle to independently follow the planned path with minimized swept volume. This meticulous approach not only reduces space utilization but also enhances maneuverability and safety in congested and complex environments.

Key contributions include:

1. Unified Trajectory Planning and Control: By leveraging SDF path planning and MPC, the proposed method allows comprehensive optimization of trajectories, ensuring reduced swept volume and precise navigation.
2. Independent Control of Wheel Steering: The methodology involves calculating steering angles for each wheel group based on velocity vectors, allowing simplification in vehicle modeling and effective use of MPC.
3. Real-Time Implementation: The paper describes a CUDA-accelerated implementation, enabling efficient computations essential for real-time applications. This aspect is crucial for practical deployments in dynamic environments.
4. Open-Source Commitment: The authors plan to make their work open-source, promoting collaboration and further advancements in autonomous systems.

Numerical Results and Discussion

In evaluating their framework, the authors present compelling findings. The proposed method achieves significant reductions in the excess swept volume, measuring only 23.14 m². This efficiency underscores the method's capability to navigate tight spaces safely, outpacing existing models such as classical truck control and hierarchical methods. The trajectory planning time is notably reduced to 1.17 seconds, further emphasizing the system's suitability for real-time applications.

Additionally, the method exhibits minimal tracking errors, maintaining lateral error and heading angle error within ±0.04 m and ±0.03°, respectively. This accuracy is critical for ensuring the AMR adheres to its path, thereby guaranteeing operational safety and efficiency.

Implications and Future Directions

The implications of this research are substantial for the field of autonomous vehicles, particularly in logistics and industrial operations where multi-axle AMRs are integral. The framework offers improved safety and control, which are pivotal for applications in warehouses, airports, and urban logistics where precise maneuverability is required.

Future research directions might explore the integration of this approach with adaptive learning algorithms, allowing AMRs to learn and improve over time. Another potential development includes extending the framework to accommodate diverse vehicle models and configurations, further enhancing its applicability across different autonomous vehicle domains.

In conclusion, this paper provides a detailed and well-substantiated methodology for enhancing trajectory planning and control in multi-axle swerve-drive AMRs. The integration of advanced path planning techniques with real-time MPC offers a robust foundation for future research, with immediate practical implications for AMR deployment in various sectors.