Efficient logistics and transport at the port heavily relies on efficient AGV scheduling and planning for container transshipment. This paper presents a comprehensive approach to address the challenges in AGV path planning and coordination within the domain of intelligent transportation systems. We propose an enhanced graph search method for constructing the global path of a single AGV that mitigates the issues associated with paths closely aligned with obstacle corner points. Moreover, a centralized global planning module is developed to facilitate planning and scheduling. Each individual AGV establishes real-time communication with the upper layers to accurately determine its position at complex intersections. By computing its priority sequence within a coordination circle, the AGV effectively treats the high-priority trajectories of other vehicles as dynamic obstacles for its local trajectory planning. The feasibility of trajectory information is ensured by solving the online real-time Optimal Control Problem (OCP). In the trajectory planning process for a single AGV, we incorporate a linear programming-based obstacle avoidance strategy. This strategy transforms the obstacle avoidance optimization problem into trajectory planning constraints using Karush-Kuhn-Tucker (KKT) conditions. Consequently, seamless and secure AGV movement within the port environment is guaranteed. The global planning module encompasses a global regulatory mechanism that provides each AGV with an initial feasible path. This approach not only facilitates complexity decomposition for large-scale problems, but also maintains path feasibility through continuous real-time communication with the upper layers during AGV travel. A key advantage of our progressive solution lies in its flexibility and scalability. This approach readily accommodates extensions based on the original problem and allows adjustments in the overall problem size in response to varying port cargo throughput, all without requiring a complete system overhaul.

The effectiveness of model-driven trajectory generation and control methods for an automated vehicle is closely related to the accuracy of vehicle kinematics and dynamics. However, the dynamic behavior of a vehicle is time-varying and uncertain, especially during high mobility driving. For the data-driven end-to-end trajectory generation and control approach, the transferability and robustness of the trained neural network cannot easily be guaranteed. In this paper, a combined model- and learning-based trajectory generation and control framework for automated overtaking maneuver is presented. Unlike the model-driven approach and end-to-end learning approach, the proposed framework blends the single-track vehicle model of the automated vehicle with time-series driving data to generate safe and high-quality overtaking motions. Specifically, an efficient vectorized implementation of multivariate Gaussian process regression (MGPR) is applied to learn the unmodeled dynamics in hopes of shortening the deviation between the single-track dynamic model and the true vehicle dynamics. A hierarchical nonlinear model predictive control with MGPR is designed for realizing local trajectory generation and control in the overtaking maneuvers. Multi-phases automated overtaking maneuvers at different speed ranges are conducted on roads with different friction coefficients to illustrate the effectiveness of the proposed trajectory generation and control framework.

In recent decades, mobile robot motion planning has seen significant advancements. Both search-based and sampling-based methods have demonstrated capabilities to find feasible solutions in complex scenarios. Mainstream path planning algorithms divide the map into occupied and free spaces, considering only planar movement and ignoring the ability of mobile robots to traverse obstacles in the $z$-direction. Additionally, paths generated often have numerous bends, requiring additional smoothing post-processing. In this work, a fast, and direct motion planning method based on continuous curvature integration that takes into account the robot's obstacle-crossing ability under different parameter settings is proposed. This method generates smooth paths directly with pseudo-constant velocity and limited curvature, and performs curvature-based speed planning in complex 2.5-D terrain-based environment (take into account the ups and downs of the terrain), eliminating the subsequent path smoothing process and enabling the robot to track the path generated directly. The proposed method is also compared with some existing approaches in terms of solution time, path length, memory usage and smoothness under multiple scenarios. The proposed method is vastly superior to the average performance of state-of-the-art (SOTA) methods, especially in terms of the self-defined $\mathcal {S}_{2}$ smoothness (mean angle of steering). Furthermore, simulations and experiments are conducted on our self-designed wheel-legged robot with 2.5-D traversability. These results demonstrate the effectiveness and superiority of the proposed approach in several representative environments.

Due to the limitation of space rover onboard computing resources and energy, there is an urgent need for high-quality drive trajectories in complex environments, which can be provided by delicately designed motion optimization methods. The nonconvexity of the collision avoidance constraints poses a significant challenge to the optimization-based motion planning of nonholonomic vehicles, especially in unstructured cluttered environments. In this paper, a novel obstacle decomposition approach, which swiftly decomposes nonconvex obstacles into their constituent convex substructures while concurrently minimizing the proliferation of resultant subobstacles, is proposed. A safe convex corridor construction method is introduced to formulate the collision avoidance constraints. The numerical approximation methods are applied to transfer the resulting continuous motion optimization problem to a nonlinear programming problem (NLP). Simulation experiments are conducted to illustrate the feasibility and superiority of the proposed methods over the rectangle safe corridor method and the area method.

The motion planning module is the core module of the automated vehicle software system, which plays a key role in connecting its preceding element, i.e., the sensing module, and its following element, i.e., the control module. The design of an adaptive polar lattice-based local obstacle avoidance (APOLLO) algorithm proposed in this paper takes full account of the characteristics of the vehicle’s sensing and control systems. The core of our approach mainly consists of three phases, i.e., the adaptive polar lattice-based local search space design, the collision-free path generation and the path smoothing. By adjusting a few parameters, the algorithm can be adapted to different driving environments and different kinds of vehicle chassis. Simulations show that the proposed method owns strong environmental adaptability and low computation complexity.

Wheel-legged robots with integrated manipulators hold great promise for mobile manipulation in logistics, industrial automation, and human-robot collaboration. However, unified control of such systems remains challenging due to the redundancy in degrees of freedom, complex wheel-ground contact dynamics, and the need for seamless coordination between locomotion and manipulation. In this work, we present the design and whole-body motion control of an omnidirectional wheel-legged quadrupedal robot equipped with a dexterous manipulator. The proposed platform incorporates independently actuated steering modules and hub-driven wheels, enabling agile omnidirectional locomotion with high maneuverability in structured environments. To address the challenges of contact-rich interaction, we develop a contact-aware whole-body dynamic optimization framework that integrates point-contact modeling for manipulation with line-contact modeling for wheel-ground interactions. A warm-start strategy is introduced to accelerate online optimization, ensuring real-time feasibility for high-dimensional control. Furthermore, a unified kinematic model tailored for the robot's 4WIS-4WID actuation scheme eliminates the need for mode switching across different locomotion strategies, improving control consistency and robustness. Simulation and experimental results validate the effectiveness of the proposed framework, demonstrating agile terrain traversal, high-speed omnidirectional mobility, and precise manipulation under diverse scenarios, underscoring the system's potential for factory automation, urban logistics, and service robotics in semi-structured environments.