The application scenarios of automated robots are undergoing a paradigm shift from structured environments to unstructured, complex settings. In highly constrained settings like factory inspections or disaster rescue, conventional steering systems show clear drawbacks. While the four-wheel independent drive and independent steering (4WS) robot provides a variety of steering modes, which can effectively meet the needs of complex environments. However, how a 4WS robot autonomously selects different steering modes based on trajectory point information during trajectory tracking remains a challenging problem. This paper proposes a multi-modal trajectory tracking method considering the switch of steering modes, which decomposes the trajectory tracking task into two parts: mode decision-making and tracking control. The corresponding method is designed based on deep reinforcement learning. Additionally, a target trajectory random generator and corresponding training interaction environment are designed to train the model in a data-driven manner. In the designed scenario, our tracker achieve more than a 30% improvement in average tracking error across all motion modes compared with model predictive control, and the decider’s average decision position error is less than 2 cm. Extensive experiments demonstrate that our method achieves superior tracking performance and real-time capabilities compared to current methods.

Regular temperature measurement of critical parts of an annealing furnace has always been a difficult task. Due to the harsh environment of high temperature, high noise, and darkness in the annealing furnace operation area, unmanned vehicles equipped with the RGB-T semantic segmentation model are usually adopted in most factories for inspection. However, existing RGB-T semantic segmentation models usually rely on good lighting or thermal conditions, which are generally difficult to fulfill in annealing furnace operation areas. In this paper, we propose a new hierarchical fusion-based semantic enhancement network, HFSENet. We first adopt the two-stream structure and the siamese structure to extract the low-level and high-level features of unimodal modalities, respectively. Then, considering the differences between the features in different hierarchical levels, we introduce a novel low-level feature spatial fusion module and a high-level feature channel fusion module to perform the multi-modal feature hierarchical fusion. On this basis, we also propose the semantic feature complementary enhancement module, which utilizes the appearance information set and object information set extracted from RGB and thermal infrared (TIR) branches to enhance the fused features and give them more semantic information. Finally, segmentation results with refined edges are obtained by an edge refinement decoder that includes a local search extraction module. The unmanned inspection vehicle we built with the proposed HFSENet has successfully passed the test, and the recognition performance of the four targets exceeds the current state-of-the-art (SOTA) method on our homemade annealing furnace operation area dataset.

With the rapid advancement of intelligent manufacturing and the rise of emerging markets, global auto-mobile exports have surged, placing unprecedented demands on logistics infrastructure. Efficient coordination of multiple robots for vehicle autonomous transfer is essential in high-density storage environments. However, conventional navigation mode, where autonomous robots navigate the entire space, often leads to inefficiencies, congestion, and increased safety risks. To address these challenges, this paper proposes a dynamic network topology framework to optimize large-scale vehicle transfers in high-density environments. The approach models free space as a network graph with directional, weighted movement costs. Leveraging yard operational characteristics, real-time transfer conditions, and robot specific capabilities, we introduce an event-triggered mechanism to update the network topology dynamically. This method continuously refines drivable space, effectively integrating yard areas with roadways to enhance routing flexibility in robot scheduling. Scenario-Based evaluations demonstrate that the proposed approach reduces traveled distance by up to 12.3% and task completion time by 19.3% compared to traditional operational networks, leading to lower operational costs and improved task efficiency. Notably, these benefits become more pronounced as the number of robots increases and the operational environment grows more complex.

Modern autonomous systems face significant challenges in coordinating multiple agents within complex environments, especially under non-holonomic constraints and kinodynamic motion planning demands. In particular, four-wheel independent drive/steering (4WIDS) robots, with their multimodal locomotion and non-holonomic kinematics, introduce new complexity to multi-agent pathfinding problems in continuous space. This study addresses the critical challenge of generating collision-free, dynamically feasible paths for multi-4WIDS robot systems. We propose a hierarchical MAPF framework that explicitly tackles two core limitations in existing methods: (1) a lack of physically realistic inter-agent conflict modeling, and (2) the inability to account for motion mode switching costs in kinodynamic path planning. At the upper level, we introduce a continuous-space binary conflict tree for resolving space-time collisions using physical dimensions and motion feasibility. At the lower level, we implement a hybrid A* search over a motion primitive database that considers mode-switch penalties and kinematic constraints. To improve search performance, we introduce two novel mechanisms: a focused search factor for directional exploration and an adaptive heuristic weighting factor to balance optimality and computational speed. Extensive experiments, including benchmark comparisons, ablation studies, and sensitivity analysis, validate the proposed method’s superior performance in inter-agent conflict resolution, real-time feasibility, and path quality.

In the labor-intensive Roll-On/Roll-Off (RO/RO) terminal environment, research on vehicle transport robots with mobility, stability, and reliability is receiving increasing attention. This paper presents a novel control framework for a Straddle-Type Dual-Body vehicle transfer robot. Initially, fine segmentation and processing of point clouds from different areas of the robot are performed, switching perception strategies for different areas based on event triggers. For target pose estimation, a traversal-based point cloud matrix fitting algorithm is designed. Additionally, for loading and unloading operations, a docking controller based on real-time target detection is developed to ensure minimal lateral and angular errors during target docking. Finally, the proposed control framework is validated through operations of the vehicle transfer robot in outdoor RO/RO terminal yards. Experimental results indicate that the average docking error remains within 3cm, with a 6.5% reduction in docking time under the same conditions. The docking precision and stability performance of the vehicle transfer robot surpass traditional methods, demonstrating satisfactory performance.