Search

This paper presents a decentralized control policy for symmetric formations in multiagent systems. It is shown that an agents, each one pursuing its leading neighbor along the line of sight rotated by a common offset angle [alpha], eventually converge to a single point, a circle or a logarithmic spiral pattern, depending on the value of [alpha]. In the final part of the paper, we present a strategy to make the agents totally anonymous, and we discuss a potential application to coverage path planning. [DOI: 10.1115/1.2767658]

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 arid 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 peiformance. 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. [ABSTRACT FROM AUTHOR]