Degree of Contribution


Document Type

Conference Proceeding


Robotics, Visual Homing, Navigation


Computer Engineering | Robotics


The rapid exploration of unknown environments is a common application of autonomous multi-robot teams. For some types of exploration missions, a mission designer may possess some rudimentary knowledge about the area to be explored. For example, the dimensions of a building may be known, but not its floor layout or the location of furniture and equipment inside. For this type of mission, the Space- Based Potential Field (SBPF) method is an approach to multirobot exploration which leverages a priori knowledge of area bounds to determine robot motion. Explored areas and obstacles exert a repulsive force, and unexplored areas exert an attractive force. While SBPF has advantages over other methods of robot exploration in terms of simplicity and performance, inaccessible space poses a problem: it exerts a permanent attractive force, pulling robots away from useful exploration elsewhere and creating minima at its boundary. Prior research established a simple method of filling in inaccessible space as a solid obstacle once an enclosing boundary is discovered; however, this method requires the entire enclosing boundary to be discovered before it can be filled. In this paper, we propose a novel combined SBPF and frontier-based method of robot exploration called O-SBPF. Our method adds two new space classifications: open, areas known to be accessible; and occluded, areas which may be inaccessible. We describe a ray-casting approach to designate areas as open or occluded, and incorporate this designation into potential vector calculations. We then show the effectiveness of O-SBPF using ROS/Stage in worlds with inaccessible space. O-SBPF significantly outperforms SBPF in rooms with large obstacles, successfully reaching 95% coverage while SBPF becomes stuck in a minima. In less complex rooms, we show that O-SBPF generally reaches 95% coverage at the same time or before SBPF.

Publication Title

2019 IEEE Int. Conf on Robotics and Biomimetics (ROBIO19), Yunnan China

Article Number


Publication Date




Peer Reviewed




Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Included in

Robotics Commons