Abstract
The ability of a robot team to reconfigure itself is useful in many applications: for metamorphic robots to change shape, for swarm motion towards a goal, for biological systems to avoid predators, or for mobile buoys to clean up oil spills. In many situations, auxiliary constraints, such as connectivity between team members or limits on the maximum hop-count, must be satisfied during reconfiguration. In this paper, we show that both the estimation and control of the graph connectivity can be accomplished in a decentralized manner. We describe a decentralized estimation procedure that allows each agent to track the algebraic connectivity of a time-varying graph. Based on this estimator, we further propose a decentralized gradient controller for each agent to maintain global connectivity during motion.
Highlights
A mobile sensor network consists of n mobile sensors connected by links along which information flows
Applications for mobile sensor networks include target tracking [15], [25], [22], [12], formation and coverage control [1], [2], [4], [6], environmental monitoring [10], [11], [17], [20], and several others. These applications take advantage of the sensors’ ability to position themselves to maximize the information in their sensor readings
The connectivity-maintenance problem has been addressed using two different approaches: control of local connectivity measures using decentralized control schemes, and control of global connectivity measures based on centralized computations
Summary
A mobile sensor network consists of n mobile sensors (or agents) connected by links along which information flows. In this paper we are concerned with controlling the global connectivity of the network (as in the second approach above) using only local communication and decentralized computations (as in the first approach above). The key component in our solution is a decentralized power iteration algorithm that enables each agent i to compute xi, which is an estimate of the i-th component of the Fiedler eigenvector. This algorithm is scalable: the computational complexity of each agent is only proportional to its number of connections in the network.
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