Trading Safety Versus Performance: Rapid Deployment of Robotic Swarms with Robust Performance Constraints
This work addresses the challenge of rapid and safe robotic swarm deployment for applications like search and rescue, though it is incremental in applying constrained Markov Decision Processes to this domain.
The paper tackles the problem of deploying a robotic swarm to cover multiple targets within a deadline while balancing stochastic travel times and failure rates, achieving efficient algorithms that maximize success probability under robust performance constraints.
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, 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.