Assessing operator strategies for real-time replanning of multiple unmanned vehicles

Clare, Andrew S.; Maere, Pierre C.P.; Cummings, Mary L.

doi:10.3233/idt-2012-0138

Cited by 10 publications

(6 citation statements)

References 21 publications

(31 reference statements)

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“…Although it would be advantageous for the system to always run its "best" plan, a replanning rate that imposes too high a workload on the human operator and prevents value-added contributions to the plan would be a disadvantage. Cummings, Clare, and Hart (2010) and Clare, Maere, and Cummings (2012) report on replanning strategies and rates, finding that moderate rates and compliance with planner recommendations generally led to better performance and lower workloads.…”

Section: Cooperative Plannersmentioning

confidence: 99%

Human Interaction With Multiple Remote Robots

Lewis¹

2013

Reviews of Human Factors and Ergonomics

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In this chapter, I review research involving remote human supervision of multiple unmanned vehicles (UVs) using command complexity as an organizing construct. Multi-UV tasks range from foraging, requiring little coordination among UVs, to formation following, in which UVs must function as a cohesive unit. Command complexity, the degree to which operator effort increases with the number of supervised UVs, is used to categorize human interaction with multiple UVs. For systems in which each UV requires the same form of attention (O(n)), effort increases linearly with the number of UVs. For systems in which the control of one UV is dependent upon another (O(>n)), additional UVs impose greater than linear increases due to the expense of coordination. For other systems, an operator interacts with an autonomously coordinating group, and effort is unaffected by group size (O(1)). Studies of human/multi-UV interaction can be roughly grouped into O(n) supervision, involving one-to-one control of individual UVs, or O(1) commanding, in which higherlevel commands are directed to a group. Research in O(n) command has centered on roundrobin control, neglect tolerance, and attention switching. Approaches to O(1) command are divided into systems using autonomous path planning only, plan libraries, human-steered planners, and swarms. Each type of system has its advantages. Less complete work in scalable displays for multiple UVs is reviewed. Mixing levels of command is probably necessary to supervise multiple UVs performing realistic tasks. Research in O(n) control is mature and can provide quantitative and qualitative guidance for design. Interaction with planners and swarms is less mature but more critical to developing effective multi-UV systems capable of performing complex tasks. IntRoductIon In recent decades, the number of mobile unmanned vehicles (UVs) deployed in field applications has risen dramatically. Their usage offers obvious advantages of reduced costs, removing humans from harm's way, or enabling entirely new applications that were previously impossible, especially when combining many such UVs into a comprehensive system. The domains of potential application are equally diverse and range from low-cost warehouse security to interplanetary exploration. New developments in commodity hardware, which serve as low-cost replacements for otherwise expensive sensing or motion capabilities, promise to further accelerate the trend toward deploying large teams of mobile UVs. This trend, however, poses a challenge for the control of such systems.

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Section: Cooperative Plannersmentioning

confidence: 99%

Human Interaction With Multiple Remote Robots

Lewis¹

2013

Reviews of Human Factors and Ergonomics

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“…It should be noted that this assumption does not hold for all systems, as previous studies have shown that frequent human intervention can potentially have a negative impact on automation (Beck, Dzindolet, and Pierce 2005;Parasuraman and Riley 1997), as some decentralised algorithms may need time to stabilise (Walker et al 2012). The automation in the multiple UAV control test bed used to validate CHAS has been found to be provably good, but suboptimal (Choi, Brunet, and How 2009), and previous experiments have shown that a moderate rate of intervention results in higher performance than a low frequency of intervention (Clare, Maere, and Cummings 2012;Cummings, Clare, and Hart 2010).…”

Section: The Chas Sd Modelmentioning

confidence: 93%

Holistic modelling for human-autonomous system interaction

Cummings

Clare

2015

Theoretical Issues in Ergonomics Science

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For complex systems that embed automation, but also rely on human interaction for guidance and contingency management, holistic models are needed that provide for an understanding of the individual human and computer elements, and address the critical interactions of such complex systems. Discrete event simulation (DES) models and system dynamics (SD) models are two different approaches that can be used to address these requirements. Both modelling approaches can support the designers of future autonomous vehicle (AV) systems by simulating the impact of alternate designs on vehicle, operator, and system performance. However, the DES modelling approach is likely best suited for using probabilistic distributions to accurately model an operator who is a serial processor of discrete tasks, as well as an environment with randomly occurring events. The SD modelling approach is better suited for modelling continuous performance feedback that is temporally dependent and is affected by qualitative variables such as trust.

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“…For mixed-initiative teams, the interactive nature of the planning process is dependent on both the capabilities of the team members and the task or mission needs. In general, human intervention in a control loop for route planning can improve the quality of the IA-generated plan [3]. This dynamic assumes that humans and IAs have different but valuable and often complementary capabilities.…”

Section: Route Planning Mechanismsmentioning

confidence: 99%

“…Replanning frequency generally produces inconsistent effects on mission performance metrics. Some studies [4,5] have found that increased replanning frequency degrades performance, while others [3] have concluded that frequent replanning improves performance. These effects are typically attributed to changes in workload, and they interact with the replanning duration (i.e., the amount of time the human spends replanning the route during each trial) such that replanning improves performance but only if done rapidly.…”

Section: Analysis 1: Consensus In Spatial Mental Modelsmentioning

confidence: 99%

Where Do You Think You're Going?

Perelman

Evans

Schaefer

2020

J. Hum.-Robot Interact.

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Route planning is a critical behavior for human-intelligent agent (H-IA) team mobility. The scientific community has made major advances in improving route planner optimality and speed. However, human factors, such as the ability to predict and understand teammates' actions and goals, are necessary for trust development in H-IA teams. Trust is especially critical when agents' behaviors do not match human team members' expectations, or the human cannot understand the agent's underlying reasoning process. To address this issue, the artificial intelligence community has pushed toward creating human-like agent behaviors using machine learning. The problem with this approach is that we do not yet have a clear understanding of what constitutes human-like behavior across the breadth of tasks that H-IA teams undertake. This article describes an investigation and comparison of human and agent route planning behaviors, the interplay between humans and agents in collaborative planning, and the role of trust in this collaborative process. Finally, we propose a data-driven methodology for characterizing and visualizing differences among routes planned by humans and agents. This methodology provides a means to advance compatible mental model metrics and theory by informing targeted transparency manipulations, thereby improving the speed and quality of routes produced by H-IA teams. CCS Concepts: • Human-centered computing → Human computer interaction (HCI); • Computing methodologies → Planning and scheduling; • Computer systems organization → Embedded and cyber-physical systems; Robotics;

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Assessing operator strategies for real-time replanning of multiple unmanned vehicles

Cited by 10 publications

References 21 publications

Human Interaction With Multiple Remote Robots

Human Interaction With Multiple Remote Robots

Holistic modelling for human-autonomous system interaction

Where Do You Think You're Going?

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