Towards autonomous exploration with information potential field in 3D environments

Autonomous exploration is one of the key components for flying robots in 3D active perception. Fast and accurate exploration algorithms are essential for aerial vehicles due to their limited flight endurance. In this paper, we address the problem of exploring the environment and acquiring information using aerial vehicles within limited flight endurance. We propose an information potential field based method for autonomous exploration in 3D environments. In contrast to the existing approaches that only consider either the traveled distances or the information collected during exploration, our method takes into account both the traveled cost and information-gain. The next best view point is chosen based on a multi-objective function which considers information of several candidate regions and the traveled path cost. The selected goal attracts the robot while the known obstacles form the repulsive force to repel the robot. These combined force drives the robot to explore the environment. Different from planners that use all acquired global information, our planner only considers the goal selected and the nearby obstacles, which is more efficient in high-dimensional environments. Furthermore, we present a method to help the robot escape when it falls into a trapped area. The experimental results demonstrate the efficiency and efficacy of our proposed method.