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International Journal of Social Robotics

, Volume 4, Issue 1, pp 15–27 | Cite as

Regression Analysis of Multi-Rendezvous Recharging Route in Multi-Robot Environment

  • Soheil KeshmiriEmail author
  • Shahram Payandeh
Original Paper

Abstract

One of the crucial issue in the field of autonomous mobile robotics is the vitality of energy efficiency of robots and the entire system they form. By efficiency here we refer to ability of robots (or the system in which they are deployed) to maintain their survival throughout the course of the operation so as to provide themselves with the opportunity of attaining energy once needed. In this paper, issue of recharging of a group of autonomous worker robots in their working environment has been addressed. To deliver the objective, a tanker robot’s planner, capable of determining an energy supply route based on regression analysis techniques, has been implemented. Specifically we have examined the practicality of ordinary and weighted least squares (OLS and WLS respectively) as well as orthogonal least absolute values (ORLAV) regressions for recharging route computation (hence the terms Least Square Recharging Route (LSRR) and Orthogonal Recharging Route (ORR)). Studies were conducted (while examining OLS and WLS techniques) to analyze the effect of various uncertainties which may exist in location information of the robots with regards to the recharging route. It has been proven that ORLAV based planner may result to a recharging route that minimizes the cumulative sum of worker robots distance traversal during the recharging process, irrespective of tanker location. Simulations in both, environment with and without obstacles, have been conducted to examine the practicality of the techniques in contrast with fixed charging station scenario. Appropriate graphs, diagrams and tables, representing the results obtained in simulations are provided for illustrative comparisons among different techniques.

Keywords

Multi-robot systems Motion planning Least square regression Orthogonal regression Recharging route 

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Copyright information

© Springer Science & Business Media BV 2011

Authors and Affiliations

  1. 1.Experimental Robotics Laboratory, School of Engineering ScienceSimon Fraser UniversityBurnabyCanada

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