Autonomous Robotic Exploration Based on Rolling Bias-RRT Algorithm

In order to improve the efficiency and autonomy of unknown region exploration for robots, an autonomous exploration algorithm based on rolling window method and bias-RRT algorithm is proposed. The rolling window method is adopted for global exploration planning, and the bias-RRT algorithm is adopted for local path planning. To improve the exploration efficiency and path quality, the evaluation function for candidate local goals is optimized considering information gain, the distance to obstacle and turning angle. Based on artificial potential field algorithm, the growth of an RRT is guided to avoid obstacles autonomously and move forward to the sub-goal rapidly. Considering exploration efficiency and autonomous obstacle avoidance simultaneously, the extend length of RRT is adaptively tuned. The global goals are updated and backtracked to avoid getting stuck in local areas, such that the completeness of the exploration is improved. Simulation results show that compared to the classical method, the proposed autonomous exploration method can improve the exploration completeness in a convoluted environment.