Sonar-Based Human Leg Localization Using Chaos Enhanced Dynamic Neighborhood Learning-Based GSA Aided sNDT Algorithm

Abstract

The present paper proposes a new human leg localization algorithm using ultrasonic sensors in human-robot coexisting environments. The algorithm estimates the motion of a human leg pair between two successive sonar scans by using a new <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">static cluster elimination</i> method, an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">edge feature based leg recognition</i> algorithm and an advanced scan matching technique. We also propose a novel, robust approach to overcome <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bad initialization</i> problem in sonar scan matching, by introducing a metaheuristic search based optimization algorithm for the sonar NDT (sNDT) method. The recently proposed dynamic neighborhood learning-based GSA (DNLGSA) has been successfully utilized in real-life scenario to solve this problem. The work also proposes a new chaos enhanced DNLGSA (CEDNLGSA) to further improve real-life performance and the proposed novel variant of the sNDT method based on CEDNLGSA, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Chaotic Metaheuristic Search Based sNDT</i> (CMHS-sNDT), has been demonstrated to achieve superior leg detection performance in various real-life case studies, compared to different contemporary state-of-the-art methods.