Abstract
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.
Highlights
Path planning for mobile robot, which is an important content in the field of intelligent robot research, is typically stated as finding a sequence of state transitions for the robot from its initial state to some desired goal state
In order to ensure the safety of the robot, we connect the current location of the moving obstacle and the predicted collision point with a line segment, and define the area, which consists of the states on the line segment, as restricted area that forbidden to pass
When A* algorithm is used to solve the path planning problem of mobile robot in dynamic environment, it will search an optimal path from the current location of the robot to the goal as long as the robot’s sensors detect a moving obstacle and a possible collision between the robot and the moving obstacle is predicted
Summary
Path planning for mobile robot, which is an important content in the field of intelligent robot research, is typically stated as finding a sequence of state transitions for the robot from its initial state to some desired goal state. In order to ensure the safety of the robot, we connect the current location of the moving obstacle and the predicted collision point with a line segment (represented by OC in Figure 4), and define the area, which consists of the states on the line segment, as restricted area that forbidden to pass. If the gray-shaded grid D is the local optimal target state and p(D) = C, the robot cannot avoid collision with the moving obstacle if it moves from the current location through D to G In this case, the obtained local optimal target state on the escape boundary is unavailable; it is necessary to choose a new local target state for the robot. For each available neighboring state X of R, compute the estimated path cost from R through X to G by equation [9], select the one with the minimum path cost value as the local optimal target
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