A new robot navigation algorithm for dynamic unknown environments based on dynamic path re-computation and an improved scout ant algorithm

A difficult issue in robot navigation or path planning in an unknown environment with static or dynamic obstacles is to find a globally optimal path from the start to the target point and at the same time avoid collisions. We present a novel and effective robot navigation algorithm for dynamic unknown environments based on an improved ant-based algorithm. In our approach two bidirectional groups of scout ants cooperate with each other to find a local optimal static navigation path within the visual domain of the robot. The robot then predicts the collision points with moving objects, and the scout ants find a new collision-free local navigation path. This process is carried out dynamically after each step of the robot, thereby allowing the robot to adjust its path as new obstacles come into view or existing obstacles move in new directions. Therefore, the robot can not only avoid collision but also make its paths globally optimal or near-optimal by making a series of dynamic adjustments to locally optimal paths. The simulation results have shown that the algorithm has good effect, high real-time performance, and is very suitable for real-time navigation in complex and dynamic environments.