The A* algorithm has been widely investigated and applied in path planning problems, but it does not fully consider the safety and smoothness of the path. Therefore, an improved A* algorithm is presented in this paper. Firstly, a new environment modeling method is proposed in which the evaluation function of A* algorithm is improved by taking the safety cost into account. This results in a safer path which can stay farther away from obstacles. Then a new path smoothing method is proposed, which introduces a path evaluation mechanism into the smoothing process. This method is then applied to smoothing the path without safety reduction. Secondly, with respect to path planning problems in complex terrains, a complex terrain environment model is established in which the distance and safety cost of the evaluation function of the A* algorithm are converted into time cost. This results in a unification of units as well as a clarity in their physical meanings. The simulation results show that the improved A* algorithm can greatly improve the safety and smoothness of the planned path and the movement time of the robot in complex terrain is greatly reduced.

Searching the lowest-cost path through a graph is central to many problems, including path planning for a mobile robot. By combining Dijkstra’s algorithm, A* algorithm, and rolling window principle, a new rapid path planning algorithm for a mobile robot in dynamic environment is proposed. First, Dijkstra’s algorithm is applied to find an initial path from the initial state to the goal. As a robot moves along the path, if a possible collision is predicted, a local optimal target state within the detection range of the sensors is selected using the rolling window principle. Then, an optimal path from the robot’s current location to this local target is searched through A* algorithm and a new path which leads the robot to move from current location to the goal is obtained. Compared to other algorithms, such as ant colony optimization algorithm, A* algorithm, and D* algorithm, the proposed algorithm can always find an optimal path during re-planning and at the meantime greatly reduce the re-planning time. The simulation results prove the feasibility and effectiveness of the proposed algorithm.