Abstract
To improve the efficiency of mobile robot movement, this paper investigates the fusion of the A* algorithm with the Dynamic Window Approach (DWA) algorithm (IA-DWA) to quickly search for globally optimal collision-free paths and avoid unknown obstacles in time. First, the data from the odometer and the inertial measurement unit (IMU) are fused using the extended Kalman filter (EKF) to reduce the error caused by wheel slippage on the mobile robot's positioning and improve the mobile robot's positioning accuracy. Second, the prediction function, weight coefficients, search neighborhood, and path smoothing processing of the A* algorithm are optimally designed to incorporate the critical point information in the global path into the DWA calculation framework. Then, the length of time and convergence speed of path planning are compared and simulated in raster maps of different complexity. In terms of path planning time, the algorithm reduces by 23.3% compared to A*-DWA; in terms of path length, the algorithm reduces by 1.8% compared to A*-DWA, and the optimization iterations converge faster. Finally, the reliability of the improved algorithm is verified by conducting autonomous navigation experiments using a ROS (Robot Operating System) mobile robot as an experimental platform.