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

Article Type

Research Article

Publication Title

IEEE Transactions on Instrumentation and Measurement

Abstract

The present article proposes a new human leg localization (HLL) 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 static cluster elimination (SCE) method, an edge feature-based leg recognition algorithm, and an advanced scan matching technique. We also propose a novel, robust approach to overcome bad initialization problem in sonar scan matching, by introducing a metaheuristic search (MHS)-based optimization algorithm for the sonar normal distributions transform (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 chaotic MHS-based sNDT (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.

DOI

10.1109/TIM.2022.3216846

Publication Date

1-1-2022

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