Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/164740
Title: Human-machine telecollaboration accelerates the safe deployment of large-scale autonomous robots during the COVID-19 pandemic
Authors: Hu, Zhongxu
Zhang, Yiran
Li, Qinghua
Lv, Chen
Keywords: Engineering::Mechanical engineering
Issue Date: 2022
Source: Hu, Z., Zhang, Y., Li, Q. & Lv, C. (2022). Human-machine telecollaboration accelerates the safe deployment of large-scale autonomous robots during the COVID-19 pandemic. Frontiers in Robotics and AI, 9, 853828-. https://dx.doi.org/10.3389/frobt.2022.853828
Project: SUG-NAP
SERC 1922500046
AN-GC-2020-012
Journal: Frontiers in Robotics and AI
Abstract: Robots are increasingly used in today’s society (Torresen, 2018; Scassellati and Vázquez, 2020). Although the “end goal” is to achieve full autonomy, currently the smartness and abilities of robots are still limited. Thus, some form of human supervision and guidance is still required to ensure robust deployment of robots in society, especially in complex and emergency situations (Nunes et al., 2018; Li et al., 2019). However, when supervising robots, human performance and ability could be affected by their varying physical and psychological states, as well as other irrelevant activities. Thus, involving people in the operation of robots introduces some uncertainties and safety issues. Currently, the supervision of robots has become even more challenging, especially due to the COVID-19 pandemic (Feizi et al., 2021). This is because, apart from maintaining the operator’s performance, avoiding close contact between the operator and others in the working place, to keep them away from potential onsite hazards, imposes new challenges. In this context, teleoperation, which keeps humans in the control loop but at a distance, provides a solution. However, this method has limitations. Under teleoperation, continuous human supervision and control are required, resulting in a higher workload. As operators observe the environment through monitors, their fields of view are limited. They can hardly feel the motion, forces, and vibrations that the robots receive on the remote side, which further restricts their situational awareness during supervision and affects the safe deployment of robots. To overcome the above limitations and further advance safe robot operation, we propose a human–machine telecollaboration paradigm that features bidirectional performance augmentation with hybrid intelligence. In contrast to the existing teleoperation architecture, where a robot only receives and executes human commands in a single direction, the telecollaboration, as shown in Figure 1, fuses and leverages the hybrid intelligence from both human and robot sides, and keeps human operators away from onsite hazards, thereby improving the overall safety, efficiency, and robustness of the human-robot system, enabling the robust deployment of large-scale autonomous robots in society. This technology has been successfully implemented in over 200 Alibaba’s autonomous delivery robots. They are now serving more than 160 communities across 52 cities in China during the COVID-19 pandemic (Alibaba Group’s). With remote help from human operators, one robot can deliver up to 300 packages with more than 40 million obstacle detections and over 5,000 interactions per day. Recently, the fleet of delivery robots reached its milestone of one million parcel deliveries.
URI: https://hdl.handle.net/10356/164740
ISSN: 2296-9144
DOI: 10.3389/frobt.2022.853828
Schools: School of Mechanical and Aerospace Engineering 
Rights: © 2022 Hu, Zhang, Li and Lv. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles

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