Authors: Seabin Lee, Joonyeol Sim, Changjoo Nam

Abstract: Multi-robot systems deployed in logistics and in- spection domains often suffer from navigation conflicts and deadlocks in dense environments such as warehouses and shopping malls. This paper presents a scalable Multi-Robot Task Allocation via Robot Redistribution Mechanism (MRTA- RM) which integrates robot path information to proactively reduce collisions and alleviate potential deadlocks, thereby enabling faster and more reliable task completion. By construct- ing a roadmap using a Generalized Voronoi Diagram (GVD), performing demand-supply analysis, and redistributing robots across environment components, MRTA-RM prevents head-on conflicts and reduces navigation congestion. Extensive simula- tions with hundreds of robots in warehouse-like environments demonstrate significant improvements in makespan, success rate, and scalability compared to state-of-the-art methods. The results highlight the potential of MRTA-RM as a conflict-aware task allocation strategy that significantly mitigates deadlocks, making it suitable for multi-robot logistics and inspection applications.

PDF

Authors: Tianxin Hu

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 crit- ical challenge: managing and minimizing 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 and more efficient autonomous systems.

PDF

Authors: Tianxin Hu

Abstract: Multi-axle Swerve-drive Autonomous Mobile Robots (MS-AGVs) equipped with independently steerable wheels are commonly used for high-payload transportation. In this work, we present a novel model predictive control (MPC) method for MS-AGV trajectory tracking that takes tire wear minimization consideration in the objective function. To speed up the problem solving process, we propose a hierarchical controller design and simplify the dynamic model by integrating the magic formula tire model and simplified tire wear model. In the experiment, the proposed method can be solved by simulated annealing in real-time on a normal personal computer and by incorporating tire wear into the objective function, tire wear is reduced by 19.19% while maintaining the tracking accuracy in curve-tracking experiments. In the more challenging scene, where the desired trajectory is offset by 60 degrees from the vehicle’s heading, the reduction in tire wear increased to 65.20% compared to the kinematic model without considering the tire wear optimization.

PDF

Authors: Boyang Lou

Abstract: LiDAR-Inertial Odometry (LIO) is widely used for autonomous navigation, but its deployment on Size, Weight, and Power (SWaP)-constrained platforms remains challeng- ing due to the computational cost of processing dense point clouds. Conventional LIO frameworks rely on a single on- board processor, leading to computational bottlenecks and high memory demands, making real-time execution difficult on embedded systems. To address this, we propose QLIO, a multi-processor distributed quantized LIO framework that reduces computational load and bandwidth consumption while maintaining localization accuracy. QLIO introduces a quantized state estimation pipeline, where a co-processor pre-processes LiDAR measurements, compressing point-to-plane residuals before transmitting only essential features to the host processor. Additionally, an rQ-vector-based adaptive resampling strategy intelligently selects and compresses key observations, further reducing computational redundancy. Real-world evaluations demonstrate that QLIO achieves a 14.1× reduction in per- observation residual data while preserving localization accu- racy. Furthermore, we release an open-source implementation to facilitate further research and real-world deployment. These results establish QLIO as an efficient and scalable solution for real-time autonomous systems operating under computational and bandwidth constraints.

PDF