Abstract
A robot swarm is a collection of simple robots designed to work together to carry out some task. Such swarms rely on the simplicity of the individual robots; the fault tolerance inherent in having a large population of identical robots; and the self-organised behaviour of the swarm as a whole. Although robot swarms present an attractive solution to demanding real-world applications, designing individual control algorithms that can guarantee the required global behaviour is a difficult problem. In this paper we assess and apply the use of formal verification techniques for analysing the emergent behaviours of robotic swarms. These techniques, based on the automated analysis of systems using temporal logics, allow us to analyse whether all possible behaviours within the robot swarm conform to some required specification. In particular, we apply model-checking, an automated and exhaustive algorithmic technique, to check whether temporal properties are satisfied on all the possible behaviours of the system. We target a particular swarm control algorithm that has been tested in real robotic swarms, and show how automated temporal analysis can help to refine and analyse such an algorithm.
Highlights
The use of autonomous robots has become increasing appealing in areas which are hostile to humans such as underwater environments, contaminated areas, and space, or where direct human control is infeasible due to the complexity or speed of the robot interactions [1, 2, 3]
The choice of fair asynchrony seems to be the obvious choice because, with real robots moving in the world, there is no global clock that the robots have access to and we cannot guarantee that each robot will move at exactly the same speed since their physical components, e.g. motors, wheels, etc., may have small differences
In this paper we have shown how formal verification can be used as part of the development of reliable robot swarm algorithms
Summary
The use of autonomous robots has become increasing appealing in areas which are hostile to humans such as underwater environments, contaminated areas, and space, or where direct human control is infeasible due to the complexity or speed of the robot interactions [1, 2, 3]. Each robot has a relatively small set of behaviours and is typically able to interact with other (nearby) robots and with its environment. Robot swarms are appealing when compared with fewer, more complex robots, in that it may be possible to design a swarm so that the failure of some of the robots will not jeopardize the overall mission, i.e. the swarm is fault tolerant. Such swarms are advantageous from a financial point of view since each robot is relatively simple and mass production can significantly reduce the fabrication costs
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