On-demand coordination of multiple service robots.
Degree: PhD, Computer Science, 2017, University of Texas – Austin
Research in recent years has made it increasingly plausible to deploy a large number of service robots in home and office environments. Given that multiple mobile robots may be available in the environment performing routine duties such as cleaning, building maintenance, or patrolling, and that each robot may have a set of basic interfaces and manipulation tools to interact with one another as well as humans in the environment, is it possible to coordinate multiple robots for a previously unplanned on-demand task? The research presented in this dissertation aims to begin answering this question.
This dissertation makes three main contributions. The first contribution of this work is a formal framework for coordinating multiple robots to perform an on-demand task while balancing two objectives: (i) complete this on-demand task as quickly as possible, and (ii) minimize the total amount of time each robot is diverted from its routine duties. We formalize this stochastic sequential decision making problem, termed on-demand multi-robot coordination, as a Markov decision Process (MDP). Furthermore, we study this problem in the context of a specific on-demand task called multi-robot human guidance, where multiple robots need to coordinate and efficiently guide a visitor to his destination.
Second, we develop and analyze stochastic planning algorithms, in order to efficiently solve the on-demand multi-robot coordination problem in real-time. Monte Carlo Tree Search (MCTS) planning algorithms have demonstrated excellent results solving MDPs with large state-spaces and high action branching. We propose variants to the MCTS algorithm that use biased backpropagation techniques for value estimation, which can help MCTS converge to reasonable yet suboptimal policies quickly when compared to standard unbiased Monte Carlo backpropagation. In addition to using these planning algorithms for efficiently solving the on-demand multi-robot coordination problem in real-time, we also analyze their performance using benchmark domains from the International Planning Competition (IPC).
The third and final contribution of this work is the development of a multi-robot system built on top of the Segway RMP platform at the Learning Agents Research Group, UT Austin, and the implementation and evaluation of the on-demand multi-robot coordination problem and two different planning algorithm on this platform. We also perform two studies using simulated environments, where real humans control a simulated avatar, to test the implementation of the MDP formalization and planning algorithms presented in this dissertation.
Advisors/Committee Members: Stone, Peter, 1971- (advisor), Grauman, Kristen (committee member), Niekum, Scott (committee member), Thomaz, Andrea (committee member), Veloso, Manuela (committee member).
Subjects/Keywords: Multi-robot coordination; Monte Carlo tree search; Markov decision processes; Probabilistic planning; Multi-robot systems
to Zotero / EndNote / Reference
APA (6th Edition):
Khandelwal, P. (2017). On-demand coordination of multiple service robots. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/61382
Chicago Manual of Style (16th Edition):
Khandelwal, Piyush. “On-demand coordination of multiple service robots.” 2017. Doctoral Dissertation, University of Texas – Austin. Accessed September 23, 2020.
MLA Handbook (7th Edition):
Khandelwal, Piyush. “On-demand coordination of multiple service robots.” 2017. Web. 23 Sep 2020.
Khandelwal P. On-demand coordination of multiple service robots. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2017. [cited 2020 Sep 23].
Available from: http://hdl.handle.net/2152/61382.
Council of Science Editors:
Khandelwal P. On-demand coordination of multiple service robots. [Doctoral Dissertation]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/61382