Mobile Sensor Network Deployment using Potential Fields: A Distributed, Scalable Solution to the Area Coverage Problem

Abstract

Abstract. This paper considers the problem of deploying a mobile sensor network in an unknown environment. A mobile sensor network is composed of a distributed collection of nodes, each of which has sensing, computation, communication and locomotion capabilities. Such networks are capable of self-deployment; i.e., starting from some compact initial configuration, the nodes in the network can spread out such that the area 'covered' by the network is maximized. In this paper, we present a potential-field-based approach to deployment. The fields are constructed such that each node is repelled by both obstacles and by other nodes, thereby forcing the network to spread itself throughout the environment. The approach is both distributed and scalable.

Indoor navigation with a swarm of flying robots

“…An alternative navigation method is to use robotic sensor networks, e.g., a pre-deployed sensor network guided outdoor flying robots in [20]. Instead of using pre-deployed networks, the robots themselves can be used as sensor nodes [21]. Various approaches to deploying robot sensor networks exist but none are suitable for indoor flying robots.…”

Deployment and Connectivity Repair of a Sensor Net with a Flying Robot

“…An overview of hardware and software requirements for sensor networks can be found in [12] which describes the Berkeley Mica Motes. Algorithms for positioning a mobile sensor network includes even dispersal of sensors from a source point and redeployment for network rebuilding [2,13]. Other important contributions include [1,4,10,15,18].…”

Robotic Swarm Dispersion Using Wireless Intensity Signals

“…A promising experiment by Howard, Mataric, and Sukhatme considers how to deploy a mobile sensor network in an unknown hostile environment [9]. They use robots equipped with a 360 o laser range finder (4 meter range).…”

“…In comparison to all of the repulsive/attractive dispersion research in which relative distance and bearing of neighboring robots is known [13,2,9], signal intensity gives only a rough approximation of distance and no bearing information. This signal intensity must be tracked over time to determine which direction the robot should move.…”

“…In particular there are many approaches that draw on the general concept of repulsive and attractive forces between robots [13,2,9]. There are subtle differences between these approaches, but the general concept is the same; move away from neighboring robots, but not too far away.…”