Multi-Robot Searching using Game-Theory Based Approach

Yan Meng

doi:10.5772/6232

Abstract

This paper proposes a game-theory based approach in a multi–target searching using a multi-robot system in a dynamic environment. It is assumed that a rough priori probability map of the targets' distribution within the environment is given. To consider the interaction between the robots, a dynamic-programming equation is proposed to estimate the utility function for each robot. Based on this utility function, a cooperative nonzero-sum game is generated, where both pure Nash Equilibrium and mixed-strategy Equilibrium solutions are presented to achieve an optimal overall robot behaviors. A special consideration has been taken to improve the real-time performance of the game-theory based approach. Several mechanisms, such as event-driven discretization, one-step dynamic programming, and decision buffer, have been proposed to reduce the computational complexity. The main advantage of the algorithm lies in its real-time capabilities whilst being efficient and robust to dynamic environments.

Highlights

It is an important and challenging field of research to design and deploy a multi-robot system to perform complex coordinate tasks, such as exploration, search and rescue, and map-building
A game-theory based strategic searching approach is proposed in this paper to cooperate a multi-robot system in a searching task
To take the interaction between the robots into consideration, a dynamic programming equation is applied to estimate the utility function, where a priori probability map and the travel costs are considered, as well as other robots’ current decisions. Based on this utility function, a utility matrix can be calculated for a N-robot nonzero-sum game, where both pure Nash Equilibrium and mixed-strategy equilibrium can be applied to guide the robots to make their decisions

Summary

Introduction

It is an important and challenging field of research to design and deploy a multi-robot system to perform complex coordinate tasks, such as exploration, search and rescue, and map-building. In the UAV search scenarios, Bertuccelli and How [Bertuccelli and How, 2005] introduced a statistical framework for an UAV searching for static targets, where the probability associated with each cell was described by a Beta distribution that was updated based on whether a target was detected or not by the sensor. This approach was extended later in [Bertuccelli and How, 2006] for an UAV search with uncertain probability maps to the case of dynamic targets, where the Beta distributions were updated using a recursive method that includes both propagation and measurement update steps. Eagle [Eagle, 1984] noted that a discrete search can be formulated as an optimization on a partially observable

Results

Discussion

Conclusion