An Efficient Robot Exploration Method Based on Heuristics Biased Sampling

Autonomous robot exploration has received widespread attention as a crucial step in constructing maps for unknown environments. Among the research results in the field of robotic exploration, the boundary exploration algorithm based on rapidly exploring random tree (RRT) performs well in most scenarios, which guides the robot motion by taking the frontier with the maximum revenue value as the target point discretely. However, this discrete target selection scheme ignores the geometric continuity of the environment, and the robot often turns to another area before completing a geometrically continuous area (e.g., a complete room), which results in the disorderly motion of the robot and a relatively low exploration efficiency. This work proposes a heuristics biased sampling-based robot exploration strategy, which utilizes the semantic information of the environment as the heuristics to guide the robot exploration. First, a lightweight network model is proposed for heuristic object recognition. Second, a geometrically continuous region is estimated based on the heuristic object, and frontiers are then extracted in this region by using a biased sampling method. Finally, the heuristic information gain model is designed to determine the target frontier for the exploration, which instructs the robot to select the frontiers in the heuristic region with a higher priority. In this way, the robot can effectively use the heuristic knowledge of the environment to improve the efficiency of exploration. Compared with the RRT-based exploration methods in simulations and real-world environmental studies, and the experimental results prove the feasibility and effectiveness of our approach.