Autonomous robotic exploration based on multiple rapidly-exploring randomized trees

Abstract

Efficient robotic navigation requires a predefined map. Various autonomous exploration strategies exist, which direct robots to unexplored space by detecting frontiers. Frontiers are boundaries separating known space form unknown space. Usually frontier detection utilizes image processing tools like edge detection, thus limiting it to two dimensional (2D) exploration. This paper presents a new exploration strategy based on the use of multiple Rapidly-exploring Random Trees (RRTs). The RRT algorithm is chosen because, it is biased towards unexplored regions. Also, using RRT provides a general approach which can be extended to higher dimensional spaces. The proposed strategy is implemented and tested using the Robot Operating System (ROS) framework. Additionally this work uses local and global trees for detecting frontier points, which enables efficient robotic exploration. Current efforts are limited to the single robot case. Extension to multi-agent systems and three-dimensional (3D) space is left for future effort.