Hui Jing

Irregular, dense, and abruptly varying obstacle distributions complicate local trajectory planning in unstructured environments by imposing stringent safety constraints under limited computational resources. We propose an adaptive rectangular-inflation method that reduces the iterations required by fixed-step safe-corridor construction. A smoothed guide path defines the corridor geometry, and a circumscribed disk conservatively approximates the vehicle footprint. A signed distance field rapidly estimates the clearance between each rectangular boundary and nearby obstacles. The estimated clearances determine both the inflation step and the priority of the four expansion directions. Within the resulting convex corridor, we formulate a Minimum Snap trajectory using differential-flatness-based parameterization and piecewise seventh-degree polynomials. A quadratic program generates the local trajectory subject to continuity, corridor-containment constraints, and derivative bounds. After optimization, a Bézier convex-hull check provides a sufficient condition for continuous corridor containment. Across the two scenarios, adaptive inflation reduced corridor-construction time by 84.55% and 87.21%, respectively, while iteration counts decreased by 90.31% and 89.36%. Comparisons with model predictive control (MPC) and Minimum Acceleration in both scenarios showed smaller jerk variations for Minimum Snap over much of the motion. The method improved corridor-construction efficiency in the evaluated static grid environments while generating local reference trajectories with smooth position, velocity, and acceleration profiles.

Compound steering combines mechanical Ackermann steering with differential drive. It offers high maneuverability and strong redundancy, making it suitable for multi-axle distributed-drive heavy vehicles. However, due to the strong nonlinearity of tires, the different dynamic responses of steering modes pose challenges for coordinated control. To address this issue, this paper focuses on an 8×8 compound steering platform driven by hub motors. A hierarchical trajectory tracking control system is designed based on an 11-degree-of-freedom nonlinear vehicle model. The control system uses a two-layer structure. The upper-layer Model Predictive Controller (MPC) handles trajectory tracking and yaw moment generation, while a longitudinal controller stabilizes the speed. The lower layer uses an optimal torque allocation strategy. It distributes the total drive force and desired yaw moment among eight wheels, coordinating differential control and traction performance. A co-simulation platform based on TruckSim and Simulink is built. The researchers conducted simulations under a typical double lane-change scenario. Results show that the proposed strategy maintains high tracking accuracy and stability even when tires enter the nonlinear region. It outperforms control methods using only Ackermann or differential steering.

Compound steering technology has been extensively adopted in military logistics and related applications, owing to its superior maneuverability and enhanced stability compared to conventional systems. To enhance the steering efficiency and dynamic response of distributed-drive unmanned platforms under low driving torque conditions, this study investigates their unique compound steering system. Specifically, a compound steering dynamics model is established, and a hierarchical stability control strategy, along with a model predictive control-based trajectory tracking algorithm, are innovatively proposed. First, a compound steering platform dynamics model is established by combining the Ackermann steering and skid yaw moment methods. Then, a trajectory tracking controller is designed using model predictive control algorithm. Finally, the additional yaw moment is calculated based on the lateral velocity error and yaw rate error, with stability control allocation performed using a fuzzy control algorithm. Comparative hardware-in-the-loop experiments are conducted for compound steering, Ackermann steering, and skid steering. The experimental results show that the compound steering technology enables unmanned platforms to achieve trajectory tracking tasks with a lower torque, faster speed, and higher efficiency.

Accuracy and nonlinear processing capabilities are critical to the positioning and navigation of autonomous vehicles in visual–inertial odometry (VIO). Existing filtering-based VIO methods struggle to deal with strongly nonlinear systems and often exhibit low precision. To this end, this paper proposes a VIO method based on the Schur complement and Iterated Extended Kalman Filtering (IEKF). The algorithm first enhances ORB (Oriented FAST and Rotated BRIEF) features using Multi-Layer Perceptron (MLP) and Transformer architectures to improve feature robustness. It then integrates visual information and Inertial Measurement Unit (IMU) data through IEKF, constructing a complete residual model. The Schur complement is applied during covariance updates to compress the state dimension, improving computational efficiency and significantly enhancing the system’s ability to handle nonlinearities while maintaining real-time performance. Compared to traditional Extended Kalman Filtering (EKF), the proposed method demonstrates stronger stability and accuracy in high-dynamic scenarios. The experimental results show that the algorithm achieves superior state estimation performance on several typical visual–inertial datasets, demonstrating excellent accuracy and robustness.