Balancing human and inter-agent influences for shared control of bio-inspired collectives

Brown, Daniel S.; Jung, Shinyoung; Goodrich, Michael A.

doi:10.1109/smc.2014.6974582

Cited by 8 publications

(8 citation statements)

References 15 publications

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“…Preliminary work on formalizing the theory of span and persistence has produced inconclusive results (Brown, Jung, & Goodrich, 2014). Thus, we feel that this is an important area for further research with many open questions.…”

Section: Discussionmentioning

confidence: 96%

“…9(a) left) results in too many mediators required to form simple polygons; adapted from . mediators are required is that the agents in the torus have too much influence over each other (Brown, Jung, & Goodrich, 2014); they "see" each other across the mediators and the resulting inter-agent influence tends to overpower the influence of the mediators (Fig. 9(a) left).…”

Section: Implication E: Managing a Torusmentioning

confidence: 99%

“…Given the understanding between span and persistence, results indicated something that is unsurprising. If the stakeholders in a torus moved too quickly in order to track a moving point, they either broke away from the torus or they caused the torus to turn into a flockexamples of too little and too much persistence, respectively (Brown, Jung, & Goodrich, 2014).…”

Section: Invariant Two: Span and Persistencementioning

confidence: 99%

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Two Invariants of Human Swarm Interaction

Brown

Goodrich

Jung

et al. 2015

Journal of Human-Robot Interaction

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The search for invariants is a fundamental aim of scientific endeavors. These invariants, such as Newton's laws of motion, allow us to model and predict the behavior of systems across many different problems. In the nascent field of Human-Swarm Interaction (HSI), a systematic identification of fundamental invariants is still lacking. Discovering and formalizing these invariants will provide a foundation for developing, and better understanding, effective methods for HSI. We propose two invariants underlying HSI for geometric-based swarms: (1) collective state is the fundamental percept associated with a bio-inspired swarm, and (2) a human's ability to influence and understand the collective state of a swarm is determined by the balance between the span and persistence. We provide evidence of these invariants by synthesizing much of our previous work in the area of HSI with several new results, including a novel user study where users manage multiple swarms simultaneously. We also discuss how these invariants can be applied to enable more efficient and successful teaming between humans and bio-inspired collectives and identify several promising directions for future research into the invariants of HSI.

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Section: Discussionmentioning

confidence: 96%

Section: Implication E: Managing a Torusmentioning

confidence: 99%

Section: Invariant Two: Span and Persistencementioning

confidence: 99%

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Two Invariants of Human Swarm Interaction

Brown

Goodrich

Jung

et al. 2015

Journal of Human-Robot Interaction

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“…From the HSI perspective, one must investigate the additional questions: What is the overall JHSL objective and what is required to solve it efficiently (Carrillo-Zapata et al, 2020;Cooke, Gorman, Myers, & Duran, 2013;Hollnagel & Woods, 2005;Hutchins, 1995;Penders et al, 2011;Rasmussen et al, 1994)? How can the capabilities of humans and swarm be combined to solve the JHSL task including task sharing and task allocation (Brown, Jung, & Goodrich, 2014;Coppin & Legras, 2011;Hutchins, 1995;Kolling et al, 2015;Music´& Hirche, 2017;Nam et al, 2019;Parasuraman, Sheridan, & Wickens, 2000;Walker et al, 2013)? How can the JHSL coordinate with external systems (Rasmussen et al, 1994)?…”

Section: Holistic Perspective On Hsimentioning

confidence: 99%

The design of self-organizing human–swarm intelligence

Hasbach

Bennewitz

2021

Adaptive Behavior

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Human–swarm interaction is a frontier in the realms of swarm robotics and human-factors engineering. However, no holistic theory has been explicitly formulated that can inform how humans and robot swarms should interact through an interface while considering real-world demands, the relative capabilities of the components, as well as the desired joint-system behaviours. In this article, we apply a holistic perspective that we refer to as joint human–swarm loops, that is, a cybernetic system made of human, swarm and interface. We argue that a solution for human–swarm interaction should make the joint human–swarm loop an intelligent system that balances between centralized and decentralized control. The swarm-amplified human is suggested as a possible design that combines perspectives from swarm robotics, human-factors engineering and theoretical neuroscience to produce such a joint human–swarm loop. Essentially, it states that the robot swarm should be integrated into the human’s low-level nervous system function. This requires modelling both the robot swarm and the biological nervous system as self-organizing systems. We discuss multiple design implications that follow from the swarm-amplified human, including a computational experiment that shows how the robot swarm itself can be a self-organizing interface based on minimal computational logic.

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“…For example, in order to obtain safe and intuitive assistance, an approach to the allocation of control authority is achieved using a human-inspired decision-making model (10). The authors of [47] treat the problem of shared control of a mobile assistive robot (MAR) by solving simultaneously three low level sub-tasks: follow a path, avoid collisions and mitigate human fatigue.…”

Section: Control For the Overlapping Interactionmentioning

confidence: 99%

Control sharing in human-robot team interaction

Musić

Hirche

2017

Annual Reviews in Control

122

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The interaction between humans and robot teams is highly relevant in many application domains, for example in collaborative manufacturing, search and rescue, and logistics. It is well-known that humans and robots have complementary capabilities: Humans are excellent in reasoning and planning in unstructured environments, while robots are very good in performing tasks repetitively and precisely. In consequence, one of the key research questions is how to combine human and robot team decision making and task execution capabilities in order to exploit their complementary skills. From a controls perspective this question boils down to how control should be shared among them. This article surveys advances in humanrobot team interaction with special attention devoted to control sharing methodologies. Additionally, aspects affecting the control sharing design, such as human behavior modeling, level of autonomy and humanmachine interfaces are identified. Open problems and future research directions towards joint decision making and task execution in human-robot teams are discussed.

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Balancing human and inter-agent influences for shared control of bio-inspired collectives

Cited by 8 publications

References 15 publications

Two Invariants of Human Swarm Interaction

Two Invariants of Human Swarm Interaction

The design of self-organizing human–swarm intelligence

Control sharing in human-robot team interaction

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