Autonomous robotic exploration based on frontier point optimization and multistep path planning

Autonomous robotic exploration of an unknown environment is a key technology for robot intelligence. In order to improve the efficiency of it, we propose a strategy based on frontier point optimization and multistep path planning in this paper. In the frontier points' optimization section, we present a random frontier points' optimization (RFPO) algorithm to select the frontier point with the highest evaluation value as the target frontier point. The evaluation function of frontier points is defined by considering information gain, navigation cost, and the precision of the localization of the robots. In the path planning section, we propose a multistep exploration strategy. Instead of planning the global path from the current position of the robot to the target frontier point directly, we set a local exploration path step size. When the robot's movement distance reaches the local exploration path step size, we reselect the current optimal frontier point for path planning to reduce the possibility that the robot may take some repetitive paths. Finally, the relevant experiments are carried out to verify the effectiveness of this strategy.