A Master of Science thesis in Mechatronics Engineering by Hassan Abdul-Rahman Umari entitled, "Multi-Robot Map Exploration Based on Multiple Rapidly-Exploring Randomized Trees," submitted in May 2017. Thesis advisor is Dr. Shayok Mukhopadhyay. Soft and hard copy available.
Efficient robotic navigation requires a predefined map. In order to autonomously acquire a map, it is desired that robots have the ability to explore unknown environments with minimum cost and time, while ensuring complete map coverage. Meeting these
requirements is challenging, and has attracted a lot of research. Various autonomous map 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 (2-D) exploration. In this work we present 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. Further more, a marketbased task allocation strategy for coordination between multiple robots is adopted. Simulations and experimental results show that the proposed strategy can successfully extract frontiers, and explore the entire map in a reasonable amount of time, and with a reduced map exploration cost. It is also shown in this work that the proposed approach has the above mentioned performance benefits without substantially losing performance when compared against image processing-based frontier detection techniques in two dimensional spaces.