A prior information heuristic based robot exploration method in indoor environment

The Rapidly-exploring Random Tree (RRT) based method has been widely used in robotic exploration, which achieves better performance than other exploration methods in most scenes. However, its core idea is a greedy strategy, that is, the robot chooses the frontier with the largest revenue value as the target point regardless of the explored environment structure. It is inevitable that before a certain area is fully explored, the robot will turn to other areas to explore, resulting in the backtracking phenomenon with a relatively lower exploration efficiency. In this paper, inspired by the perception law of bionic human, a new exploration strategy is proposed on the basis of the prior information heuristic. Firstly, a lightweight network model is proposed for the recognition of the heuristic objects. Secondly, the prediction region is formed based on the position of the heuristic object, and the frontiers in this region are extracted by the method of image processing. Finally, a heuristic information gain model is designed to guide the robot to explore, which allocates priority to the frontiers within the heuristic object area, so that the robot can make effective use of the prior knowledge of the room in the scene. Priority is given to the exploration of one room completely and then to the next, which can greatly improve the efficiency of exploration. In the experimental studies, we compare our method with RRT based exploration method in different environments, and the experimental results prove the effectiveness of our method.