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Obstacle Avoidance Policies for Cluster Space Control of Nonholonomic Multirobot Systems | IEEE Journals & Magazine | IEEE Xplore

Obstacle Avoidance Policies for Cluster Space Control of Nonholonomic Multirobot Systems

Publisher: IEEE

Abstract:

The cluster space control technique promotes simplified specification and monitoring of the motion of mobile multirobot systems of limited size. In this publication, we s...View more

Abstract:

The cluster space control technique promotes simplified specification and monitoring of the motion of mobile multirobot systems of limited size. In this publication, we summarize the definition of the cluster space framework and introduce a multirobot cluster space controller specific for unicycle-like nonholonomic mobile robots. The controller produces cluster commands that translate into valid robot-level motions. We then study the closed-loop system stability in the Lyapunov sense. Two different obstacle avoidance algorithms are proposed and the stability of the resulting systems is also addressed. Experimental tests with a three-robot system and simulation results with a ten-robot system verify the functionality of the proposed approaches.
Published in: IEEE/ASME Transactions on Mechatronics ( Volume: 17, Issue: 6, December 2012)
Page(s): 1068 - 1079
Date of Publication: 07 July 2011

ISSN Information:

Publisher: IEEE

I. Introduction

Robotic systems offer many advantages for accomplishing a wide variety of tasks given their strength, speed, precision, repeatability, and ability to withstand extreme environments. Whereas most robots perform these tasks in an isolated manner, interest is growing in the use of tightly interacting multirobot systems to improve performance in current applications and to enable new capabilities. Potential advantages of multirobot systems include redundancy, increased coverage and throughput, flexible reconfigurability, and spatially diverse functionality [1]. For mobile systems, one of the key technical considerations is the technique used to coordinate the motions of the individual vehicles. A wide variety of techniques have been and continue to be explored, drawing on work in control theory, robotics, and biology [2] and applicable for robotic applications throughout land, sea, air, and space. Notable work in this area includes the use of leader–follower techniques, in which follower robots control their position relative to a designated leader [3]–[5]. A variant of this is leader–follower chains, in which follower robots control their position relative to one or more local leaders, which, in turn, are following other local leaders in a network that ultimately is led by a designated leader [6]. Several approaches employ artificial fields as a construct to establish formation keeping forces for individual robots within a formation. For example, potential fields may be used to implement repulsive forces among neighboring robots and between robots and objects in the field in order to symmetrically surround an object to be transported [7]. Potential fields and behavioral motion primitives have also been used to compute reactive robot drive commands that balance the need to arrive at the final destination, to maintain relative locations within the formation, and to avoid obstacles [8]–[10]. As another example, the virtual bodies and artificial potentials approach uses potential fields to maintain the relative distances both between neighboring robots as well as between robots and reference points, or “virtual leaders,” that define the “virtual body” of the formation [11]–[13]. Some methods rely on the definition and merging of different task functions [14], [15]. Other techniques focus on generating robot trajectories based on “rigid body” constraints [16].

References

References is not available for this document.