In this paper, we consider a stochastic deployment problem, where a robotic swarm is tasked with the objective of positioning at least one robot at each of a set of pre-assigned targets while meeting a temporal deadline. Travel times and failure rates are stochastic but related, inasmuch as failure rates increase with speed. To maximize chances of success while meeting the deadline, a control strategy has therefore to balance safety and performance. Our approach is to cast the problem within the theory of constrained Markov decision processes (CMDPs), whereby we seek to compute policies that maximize the probability of successful deployment while ensuring that the expected duration of the task is bounded by a given deadline. To account for uncertainties in the problem parameters, we consider a robust formulation and we propose efficient solution algorithms, which are of independent interest. Numerical experiments confirming our theoretical results are presented and discussed.
Trading Safety Versus Performance: Rapid Deployment of Robotic Swarms With Robust Performance Constraints
Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received February 1, 2014; final manuscript received June 7, 2014; published online October 21, 2014. Assoc. Editor: Dejan Milutinovic.
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Chow, Y., Pavone, M., Sadler, B. M., and Carpin, S. (October 21, 2014). "Trading Safety Versus Performance: Rapid Deployment of Robotic Swarms With Robust Performance Constraints." ASME. J. Dyn. Sys., Meas., Control. March 2015; 137(3): 031005. https://doi.org/10.1115/1.4028117
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