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.

Tracking dynamic targets poses a significant challenge for Unmanned Ground Vehicles (UGVs). Existing methods often lack research on multi-disturbed conditions. To address this issue, we propose a trajectory-prediction-based dynamic tracking scheme, which includes target localization, trajectory prediction, and UGV control. Firstly, an estimation algorithm based on the Extended Kalman Filter (EKF) is employed to mitigate noise and estimate the absolute states of the target accurately. To enhance robustness, we present an Adaptive Trajectory Prediction (ATP) algorithm based on prediction anchors. In this method, a quantization standard for trajectory disturbance is designed for adaptive control. Subsequently, we iteratively solve prediction anchor points based on two motion models to robustly predict the target trajectory even in the presence of unknown disturbances. Finally, the Linear Time-Varying Model Predictive Control (LTV-MPC) is utilized in the UGV controller for dynamic tracking. Experimental results demonstrate that the ATP exhibits superior prediction robustness and accuracy in perturbed environments compared to other prediction algorithms. In addition, the proposed scheme effectively achieves dynamic tracking of the Unmanned Aerial Vehicle (UAV) by the UGV under multi-disturbed conditions. Specifically, when the target moves at a speed of 1.0 m/s, the UGV can maintain a tracking error within 0.346 m.