Performance of a Class of Multi-Robot Deploy and Search Strategies based on Centroidal Voronoi Configurations

TL;DR

This paper evaluates multi-robot deploy and search strategies based on centroidal Voronoi configurations, comparing their efficiency and convergence properties under various constraints and parameters.

Contribution

It introduces a combined deploy and search (CDS) strategy and analyzes its performance relative to existing methods using Voronoi-based deployment.

Findings

CDS outperforms greedy and random strategies in search time.

Performance depends on number of robots, speed, and sensor range.

Voronoi-based strategies effectively reduce uncertainty in search space.

Abstract

This paper considers a class of deploy and search strategies for multi-robot systems and evaluates their performance. The application framework used is a system of autonomous mobile robots equipped with required sensors and communication equipment deployed in a search space to gather information. The lack of information about the search space is modeled as an uncertainty density distribution over the search space. A {\em combined deploy and search} (CDS) strategy has been formulated as a modification to {\em sequential deploy and search} (SDS) strategy presented in our previous work. The optimal deployment strategy using Voronoi partition forms the basis for these two search strategies. The strategies are analyzed in presence of constraints on robot speed and limit on sensor range for convergence of trajectories with corresponding control laws responsible for the motion of robots. SDS and CDS strategies are compared with standard greedy and random search strategies on the basis of time taken to achieve reduction in the uncertainty density below a desired level. The simulation experiments reveal several important issues related to the dependence of the relative performances of the strategies on parameters such as number of robots, speed of robots, and their sensor range limits.