Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 28 November 2020 / Approved: 30 November 2020 / Online: 30 November 2020 (09:11:57 CET)

A peer-reviewed article of this Preprint also exists.

Zheng, K.; Wu, F.; Chen, X. Laser-Based People Detection and Obstacle Avoidance for a Hospital Transport Robot. Sensors 2021, 21, 961. Zheng, K.; Wu, F.; Chen, X. Laser-Based People Detection and Obstacle Avoidance for a Hospital Transport Robot. Sensors 2021, 21, 961.

Journal reference: Sensors 2021, 21, 961
DOI: 10.3390/s21030961

Abstract

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, performs much better than traditional methods, and can be straightforwardly extended to 3D laser data. 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.

Subject Areas

service robot; navigation; people detection; obstacle avoidance

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