Development of Fully Autonomous and Cooperative Robotic System for Interplanetary Explorations

Thumbnail Image



Journal Title

Journal ISSN

Volume Title


The Ohio State University

Research Projects

Organizational Units

Journal Issue


The next frontier of interplanetary exploration missions would encounter countless unpredictable geographical challenges including uninhabitable caves, icy craters of the Moon and Mars, unsustainable mountain cliffs, high radiation areas, and extreme temperature environments. This research will design a fully autonomous and a cooperative robotic team composed of unmanned ground vehicles (UGVs) with hybrid operational modes to tackle the multiple traveling salesman problem (mTSP) and to overcome environmental obstacles, to accomplish the challenging interplanetary exploration missions. The hybrid operational modes allow every UGV in the team to not only travel on a ground surface but also jump over obstacles, and these UGVs were named jumping rovers. The jumping capability provides a flexible form of locomotion by leaping and landing on top of obstacles instead of navigating around obstacles. Through the cooperation of heterogeneous robots, the goal is to explore unknown areas subject to extreme environmental conditions. To solve the mTSP, an optimal path between any two objective points in an mTSP is determined by the optimized rapidly-exploring random tree method, named RRT*, and is further improved through a refined RRT* algorithm to find a smoother path between targets. Then, the mTSP is formulated as a mixed-integer linear programming (MILP) problem to search for the most cost-effective combination of paths for multiple UGVs that can allocate tasks like visiting target points. The effectiveness of the hybrid operational modes and optimized motion with assigned tasks is verified in an indoor, physical experimental environment using customized jumping rovers.



Jumping Robot, Rapidly-exploring Random Tree Star (RRT*), Mixed-Integer Linear Programming (MILP), Multiple Traveling Salesman Problem (mTSP), Path and Motion Planning, Task Allocation


Published version: K. C. Tan, I. Shyu, M. Jung, C. Wan and R. Dai, "Motion Planning and Task Allocation for a Jumping Rover Team," 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, 2020, pp. 5278-5283.