Path planning and obstacle avoidance are essential for autonomous driving cars. On the base of a self-constructed smart obstacle avoidance car, which used a LeTMC-520 depth camera and Jetson controller, this paper established a map of an unknown indoor environment based on depth information via SLAM technology. The Dijkstra algorithm is used as the global path planning algorithm and the dynamic window approach (DWA) as its local path planning algorithm, which are applied to the smart car, enabling it to successfully avoid obstacles from the planned initial position and reach the designated position. The tests on the smart car prove that the system can complete the functions of environment map establishment, path planning and navigation, and obstacle avoidance.

In order to meet the performance requirements of global optimality and path smoothness in robot path planning, a new fusion algorithm of jump-A * algorithm and dynamic window approach is proposed. First, A * algorithm is optimized by using the jump point search method and a new distance evaluation function defined by Manhattan and Euclidean distance to obtain global path information. Then take the dynamic window approach as the core by integrating the global path information to safely plan a global optimal path with high smoothness. The experimental results show that the new fusion algorithm proposed in this paper can not only effectively solve the problem of non-continuous curvature and excessive turning angle at the turning points of the path planned by jump-A * algorithm, but also improve the smoothness of the path and the global optimality. This research is beneficial to the motion control of robots and has certain reference for robot navigation.INDEX TERMS Dynamic window method, jump-A * algorithm, jumping point search, path planning.

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