scholarly journals On the Safety of Mobile Robots Serving in Public Spaces

2021 ◽  
Vol 10 (3) ◽  
pp. 1-27
Author(s):  
Pericle Salvini ◽  
Diego Paez-Granados ◽  
Aude Billard

Since 2014, a specific standard has been dedicated for the safety certification of personal care robots, which operate in close proximity to humans. These robots serve as information providers, object transporters, personal mobility carriers, and security patrollers. In this article, we point out the shortcomings concerning EN ISO 13482:2014, which encompasses guidelines regarding the safety and design of personal care robots. In particular, we argue that the current standard is not suitable for guaranteeing people's safety when these robots operate in public spaces. Specifically, the standard lacks requirements to protect pedestrians and bystanders. The guideline implicitly assumes that private spaces, such as households and offices, present the same hazards as in public spaces. We highlight the existence of at least three properties pertaining to robots’ use in public spaces. These properties include (1) crowds, (2) social norms and proxemics rules, and (3) people's misbehaviours. We discuss how these properties impact robots’ safety. This article aims to raise stakeholders’ awareness on individuals’ safety when robots are deployed in public spaces. This could be achieved by integrating the gaps present in EN ISO 13482:2014 or by creating a new dedicated standard.

2017 ◽  
Author(s):  
Timo Smieszek ◽  
Gianrocco Lazzari ◽  
Marcel Salathé

ABSTRACTThere is increasing evidence that aerosol transmission is a major contributor to the spread of influenza. Despite this, virtually all studies assessing the dynamics and control of influenza assume that it is transmitted solely through direct contact and large droplets, requiring close physical proximity. Here, we use wireless sensors to measure simultaneously both the location and close proximity contacts in the population of a US high school. This dataset, highly resolved in space and time, allows us to model both droplet and aerosol transmission either in isolation or in combination. In particular, it allows us to computationally assess the effectiveness of overlooked mitigation strategies such as improved ventilation that are available in the case of aerosol transmission. While the effects of the type of transmission on disease outbreak dynamics appear to be weak, we find that good ventilation could be as effective in mitigating outbreaks as vaccinating the majority of the population. In simulations using empirical transmission levels observed in households, we find that bringing ventilation to recommended levels has the same mitigating effect as a vaccination coverage of 50% to 60%. Our results therefore suggest that improvements of ventilation in public spaces could be an important and easy-to-implement strategy supplementing vaccination efforts for effective control of influenza spread.


2020 ◽  
Vol 8 (6) ◽  
pp. 4333-4338

This paper presents a thorough comparative analysis of various reinforcement learning algorithms used by autonomous mobile robots for optimal path finding and, we propose a new algorithm called Iterative SARSA for the same. The main objective of the paper is to differentiate between the Q-learning and SARSA, and modify the latter. These algorithms use either the on-policy or off-policy methods of reinforcement learning. For the on-policy method, we have used the SARSA algorithm and for the off-policy method, the Q-learning algorithm has been used. These algorithms also have an impacting effect on finding the shortest path possible for the robot. Based on the results obtained, we have concluded how our algorithm is better than the current standard reinforcement learning algorithms


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