Special Robotics Seminar
Cooperative Robot Exploration Strategy for a Team of Quadrotor UAVs
Dr. Jinho Kim
University of Maryland, Baltimore County
This presentation presents a cooperative robot exploration strategy in an unknown environment integrated with Sensor-based Selective Nodechain (SSN) for a team of quadrotor Unmanned Aerial Vehicles (UAVs). In the previously proposed exploration strategies for mobile robots, the backtracking path is not optimized, so that the exploration strategies are inefficient by consuming more exploration time with detours. In addition, ground mobile robots cannot move to the same point on the two-dimensional (2D) plane at the same time during their exploration. Furthermore, ground robots occasionally are obstructed by one another due to topographical factors.
To resolve the limitations and drawbacks, a Cooperative Sensor-based Selective Nodechain (CSSN) exploration strategy is developed for multiple quadrotors with a merged frontier identification technique, the SSN-Merged. By employing the SSN-Merged technique, the next target node is selected with an optimized coverage of frontier using a Selective Target Node (STN) method. Furthermore, to expand the proposed strategy to the three-dimensional (3D) workspace with quadrotors, a Multiple Flight Levels (MFL) approach is proposed with an Integrated Sliding Mode Controller (ISMC) to increase the efficiency of the exploration. During the backtracking mode, when the quadrotor reaches a dead end where no frontier exists, the proposed nodechain helps to generate the optimized paths, so that the backtracking algorithm chooses the best path to backtrack efficiently.
Dr. Miao Yu
Dr. Jinho Kim received his doctoral degree and master’s degree in Mechanical Engineering at the University of Maryland, Baltimore County in 2018 and 2017, respectively. He received his bachelor’s degree in Aerospace Engineering at the Chungnam National University, South Korea in 2011. His research interests include application of nonlinear control theory, vision-based autonomous control and navigation.