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Laser-Based People Detection and Obstacle Avoidance for a Hospital Transport Robot

This paper describes the development of a laser-based people detection and obstacle avoidance algorithm for a differential-drive robot, which is used for transporting materials along a reference path in hospital domains. Detecting humans from laser data is an important functionality for the safety o...

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Autores principales: Zheng, Kuisong, Wu, Feng, Chen, Xiaoping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7867058/
https://www.ncbi.nlm.nih.gov/pubmed/33535488
http://dx.doi.org/10.3390/s21030961
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author Zheng, Kuisong
Wu, Feng
Chen, Xiaoping
author_facet Zheng, Kuisong
Wu, Feng
Chen, Xiaoping
author_sort Zheng, Kuisong
collection PubMed
description This paper describes the development of a laser-based people detection and obstacle avoidance algorithm for a differential-drive robot, which is used for transporting materials along a reference path in hospital domains. Detecting humans from laser data is an important functionality for the safety of navigation in the shared workspace with people. Nevertheless, traditional methods normally utilize machine learning techniques on hand-crafted geometrical features extracted from individual clusters. Moreover, the datasets used to train the models are usually small and need to manually label every laser scan, increasing the difficulty and cost of deploying people detection algorithms in new environments. To tackle these problems, (1) we propose a novel deep learning-based method, which uses the deep neural network in a sliding window fashion to effectively classify every single point of a laser scan. (2) To increase the speed of inference without losing performance, we use a jump distance clustering method to decrease the number of points needed to be evaluated. (3) To reduce the workload of labeling data, we also propose an approach to automatically annotate datasets collected in real scenarios. In general, the proposed approach runs in real-time and performs much better than traditional methods. Secondly, conventional pure reactive obstacle avoidance algorithms can produce inefficient and oscillatory behaviors in dynamic environments, making pedestrians confused and possibly leading to dangerous reactions. To improve the legibility and naturalness of obstacle avoidance in human crowded environments, we introduce a sampling-based local path planner, similar to the method used in autonomous driving cars. The key idea is to avoid obstacles by switching lanes. We also adopt a simple rule to decrease the number of unnecessary deviations from the reference path. Experiments carried out in real-world environments confirmed the effectiveness of the proposed algorithms.
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spelling pubmed-78670582021-02-07 Laser-Based People Detection and Obstacle Avoidance for a Hospital Transport Robot Zheng, Kuisong Wu, Feng Chen, Xiaoping Sensors (Basel) Article This paper describes the development of a laser-based people detection and obstacle avoidance algorithm for a differential-drive robot, which is used for transporting materials along a reference path in hospital domains. Detecting humans from laser data is an important functionality for the safety of navigation in the shared workspace with people. Nevertheless, traditional methods normally utilize machine learning techniques on hand-crafted geometrical features extracted from individual clusters. Moreover, the datasets used to train the models are usually small and need to manually label every laser scan, increasing the difficulty and cost of deploying people detection algorithms in new environments. To tackle these problems, (1) we propose a novel deep learning-based method, which uses the deep neural network in a sliding window fashion to effectively classify every single point of a laser scan. (2) To increase the speed of inference without losing performance, we use a jump distance clustering method to decrease the number of points needed to be evaluated. (3) To reduce the workload of labeling data, we also propose an approach to automatically annotate datasets collected in real scenarios. In general, the proposed approach runs in real-time and performs much better than traditional methods. Secondly, conventional pure reactive obstacle avoidance algorithms can produce inefficient and oscillatory behaviors in dynamic environments, making pedestrians confused and possibly leading to dangerous reactions. To improve the legibility and naturalness of obstacle avoidance in human crowded environments, we introduce a sampling-based local path planner, similar to the method used in autonomous driving cars. The key idea is to avoid obstacles by switching lanes. We also adopt a simple rule to decrease the number of unnecessary deviations from the reference path. Experiments carried out in real-world environments confirmed the effectiveness of the proposed algorithms. MDPI 2021-02-01 /pmc/articles/PMC7867058/ /pubmed/33535488 http://dx.doi.org/10.3390/s21030961 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zheng, Kuisong
Wu, Feng
Chen, Xiaoping
Laser-Based People Detection and Obstacle Avoidance for a Hospital Transport Robot
title Laser-Based People Detection and Obstacle Avoidance for a Hospital Transport Robot
title_full Laser-Based People Detection and Obstacle Avoidance for a Hospital Transport Robot
title_fullStr Laser-Based People Detection and Obstacle Avoidance for a Hospital Transport Robot
title_full_unstemmed Laser-Based People Detection and Obstacle Avoidance for a Hospital Transport Robot
title_short Laser-Based People Detection and Obstacle Avoidance for a Hospital Transport Robot
title_sort laser-based people detection and obstacle avoidance for a hospital transport robot
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7867058/
https://www.ncbi.nlm.nih.gov/pubmed/33535488
http://dx.doi.org/10.3390/s21030961
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