Abstract:Using a single human to supervise multiple robots helps to address manpower constraints while deriving the benefits of multiple-robot deployment such as efficiency and improved system reliability. However, it can also induce high supervisor workload and diminish situation awareness. This article explains workload and situation awareness. It reviews various studies related to human-robot systems to illustrate the effects and causes of workload and diminished situation awareness in such systems. The article revi… Show more
“…While the above techniques enhance the operators' control of a robot system, we focus on complex autonomous systems that require supervision and analyse how this supervising burden may be reduced. Using one operator to control multiple robots has benefits in terms of cost and control coherency but leads to a higher workload and decrease in situation awareness [29]. The human operator is often overlooked when supervising robots [30], despite their workload influencing task performance [31] and in fact having long-term negative effects on well-being [32].…”
There is a need for semi-autonomous systems capable of performing both automated tasks and supervised maneuvers. When dealing with multiple robots or robots with high complexity (such as humanoids), we face the issue of effectively coordinating operators across robots. We build on our previous work to present a methodology for designing trajectories and policies for robots such that a few operators can supervise multiple robots. Specifically, we: (1) Analyze the complexity of the problem, (2) Design a procedure for generating policies allowing operators to oversee many robots, (3) Present a method for designing policies and robot trajectories to allow operators to oversee multiple robots, and (4) Include both simulation and hardware experiments demonstrating our methodologies.
“…While the above techniques enhance the operators' control of a robot system, we focus on complex autonomous systems that require supervision and analyse how this supervising burden may be reduced. Using one operator to control multiple robots has benefits in terms of cost and control coherency but leads to a higher workload and decrease in situation awareness [29]. The human operator is often overlooked when supervising robots [30], despite their workload influencing task performance [31] and in fact having long-term negative effects on well-being [32].…”
There is a need for semi-autonomous systems capable of performing both automated tasks and supervised maneuvers. When dealing with multiple robots or robots with high complexity (such as humanoids), we face the issue of effectively coordinating operators across robots. We build on our previous work to present a methodology for designing trajectories and policies for robots such that a few operators can supervise multiple robots. Specifically, we: (1) Analyze the complexity of the problem, (2) Design a procedure for generating policies allowing operators to oversee many robots, (3) Present a method for designing policies and robot trajectories to allow operators to oversee multiple robots, and (4) Include both simulation and hardware experiments demonstrating our methodologies.
“…The research literature demonstrates the transfer of human tasks into automated tasks is essential. Yet, a transfer has not been achieved without substantial effort for the user in combination with a limited area of applications requiring detailed domain knowledge (Wong and Seet 2017;Pedersen et al 2016;Wantia et al 2016). In short, Zhao et al (2015) pointed out a knowledge gap, where the transition from an extended HTA process towards automation system design is still missing.…”
Design of automation system relies on experts' knowledge and experience accumulated from past solutions. In designing novel solutions, however, it is difficult to apply past knowledge and achieve design right-first-time, therefore wasting valuable resources and time. SADT/IDEF0 models are commonly used by automation experts to model manufacturing systems based on the manual process. However, function generalisation without benchmarking is difficult for experts particularly for complex and highly skilled-based tasks. This paper proposes a functional task abstraction approach to support automation design specification based on human factor attributes. A semi-automated clustering approach is developed to identify key functions from an observed manual process. The proposed approach is tested on five different automation case studies. The results indicate the proposed method reduces inconsistency in task abstraction when compared to the current approach that relies on the experts, which are further validated against the solutions generated by automation experts.
“…Cognitive workload, that is, the mental exertion required to carry out a task (Bailey, Scerbo, Freeman, Mikulka, & Scott, ; Edwards, ; Kirlik, ; Parasuraman & Riley, ; Wong & Seet, ; Woods, ), can also encourage users to depend more heavily on automation. RAs can reduce cognitive workload by assisting the user to process information quickly (Nielsen, ; Ruf et al, ), by conforming to the user's expectations (Nussbaumer, Matter, Reto á Porta, et al, ; Nussbaumer, Matter, & Schwabe, ; Ruf et al, , ), and being easy to understand (Ruf et al, , ).…”
Robo‐advisors, a type of automated decision aid, offer consumers a cost‐efficient alternative to traditional financial advisory services. Because aids do not always produce correct judgments, however, users may fail to act appropriately on their advice. To anticipate and protect against suboptimal aid use, designers need to understand the variables that influence automation trust and dependence, including the operators' inherent biases, and the characteristics of the automated system itself. This paper reviews the literature on human interaction with decision aids, aiming to inform the design of robo‐advisory platforms.
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