Abstract
Swarms of robots can use their sensing abilities to explore unknown environments and deploy on sites of interest. In this task, a large number of robots is more effective than a single unit because of their ability to quickly cover the area. However, the coordination of large teams of robots is not an easy problem, especially when the resources for the deployment are limited. In this paper, the Distributed Bees Algorithm (DBA), previously proposed by the authors, is optimized and applied to distributed target allocation in swarms of robots. Improved target allocation in terms of deployment cost efficiency is achieved through optimization of the DBA’s control parameters by means of a Genetic Algorithm. Experimental results show that with the optimized set of parameters, the deployment cost measured as the average distance traveled by the robots is reduced. The cost-efficient deployment is in some cases achieved at the expense of increased robots’ distribution error. Nevertheless, the proposed approach allows the swarm to adapt to the operating conditions when available resources are scarce.
Highlights
Distributed sensor networks can be used to gather information and create knowledge about an unknown environment
In applications that require area coverage, multi-robot systems with their sensing capabilities have an advantage over a single robot unit because of their ability to quickly deploy within a larger area
The Distributed Bees Algorithm (DBA), which was previously proposed by the authors, was applied for distributed target allocation
Summary
Distributed sensor networks can be used to gather information and create knowledge about an unknown environment. Due to the potential that this field has, great efforts have been made by various research groups to investigate the algorithms for coordination and control of multi-robot systems consisting of large number of units. Dudek et al [1] proposed a taxonomy that categorizes the existing multi-robot systems along various axes, including size (number of robots), team organization (e.g., centralized vs distributed), communication topology (e.g., broadcast vs unicast), and team composition (e.g., homogeneous vs heterogeneous). Gerkey and Matarić [2] categorized the underlying coordination problems with a focus on multi-robot task allocation (MRTA). The DBA introduces a set of control parameters that adapt swarm’s behavior with respect to robots’ distribution error and deployment cost In this work, these parameters are optimized for an improved swarm’s performance in terms of deployment cost measured as the average distance traveled by the robots in the deployment phase.
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