The A* algorithm is widely used in path planning for Automated Guided Vehicles (AGVs), but the path it generates is prone to collision with random obstacles. To address this issue, this paper proposes a hybrid path planning algorithm integrating the improved A* algorithm with Dynamic Window Approach (DWA). Firstly, a global key point extraction strategy is adopted, and Bresenham’s line algorithm is used to eliminate redundant path points and turning inflection points, optimizing the conciseness and continuity of the path while redefining the child nodes of the current position. Secondly, in complex environments, the inflection points of the global path are taken as the target points of DWA to segment the path, and local dynamic planning is combined to achieve real-time obstacle avoidance. Simulation results show that compared with the traditional A* algorithm, the improved algorithm reduces the planning time by 24.19%, decreases the number of inflection points by 40.00%, and shortens the path length by 1.49%. In environments with random obstacles, the path generated by the hybrid algorithm is smoother, which can effectively enhance the local obstacle avoidance capability and improve the safety of path planning. Furthermore, physical experiments on an AGV platform with a distributed master-slave control architecture (STM32 microcontroller and Jetson embedded processor) verify the algorithm’s hardware compatibility and real-time computing performance, validating its engineering applicability in practical industrial scenarios.

The A* algorithm is widely used in path planning for Automated Guided Vehicles (AGVs), but it lacks real-time obstacle avoidance capability. To address this limitation, this paper proposes a hybrid algorithm that integrates an improved A* with the Dynamic Window Approach (DWA). First, a global key-point extraction strategy is introduced: redundant and turning points are removed using Bresenham’s line algorithm to enhance path smoothness and continuity, while child nodes of the current position are redefined to improve search efficiency. Second, to strengthen obstacle avoidance in complex environments, turning points in the global path are designated as sub-targets for DWA, enabling path segmentation and local dynamic planning. This integration allows the system to react in real time to dynamic obstacles. Simulation results demonstrate that, compared to the traditional A* algorithm, the proposed method reduces planning time by 24.19%, the number of inflection points by 40.00%, and path length by 1.49%. In environments with random obstacles, the fused approach generates smoother trajectories and significantly enhances local obstacle avoidance and overall path-planning safety.

In the field of dynamic characteristic testing of automotive grade IGBT modules, high-frequency noise interference and stray inductance are universal technical challenges faced by the industry. High frequency noise and stray inductance can interfere with signal integrity, seriously reducing the testing accuracy of the platform and causing deviations in the dynamic characteristic evaluation of IGBT modules. To address this issue, this paper proposes a software hardware collaborative optimization strategy and designs a high-precision dynamic characteristic testing platform for automotive grade IGBT modules. At the software level, Savitzky–Golay filters are introduced and designed to process the test data, filter out high-frequency noise interference. After filtering, the noise mean square error and peak to peak value of the platform are generally reduced by 48–68%, and the switch ringing or peak value is reduced by about 45–53%. In terms of hardware optimization, an improved PCB laminated busbar was designed to reduce the overall stray inductance of the platform through simulation analysis of the structural parameters and via factors of the laminated busbar using ANSYS 2023 R1. The test results verification shows that the stray inductance of the testing platform is only 23 nH. The absolute error between the parameters measured by the testing platform and the reference values of the tested module is within 5%.