Executive decision support

Ding, Xu Chu; Powers, Matthew D.; Egerstedt, Magnus; Young, Shih-Yih; Balch, Tucker

doi:10.1109/mra.2009.932526

Cited by 33 publications

(9 citation statements)

References 9 publications

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“…It proposes yet another architecture based on a set of desirable features and presents a simple test system in which operators successfully aid a swarm in locating radiation sources. Also focused on a particular task, in [7] a team of Unmanned Aerial Vehicles (UAVs) is controlled by a single operator using behavior-based controls. These controls enable UAVs to perform a surveillance mission semiautonomously while the operator generates a mission plan.…”

Section: Related Workmentioning

confidence: 99%

Towards human control of robot swarms

Kolling

Nunnally

Lewis

2012

Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction

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In this paper we investigate principles of swarm control that enable a human operator to exert influence on and control large swarms of robots. We present two principles, coined selection and beacon control, that differ with respect to their temporal and spatial persistence. The former requires active selection of groups of robots while the latter exerts a passive influence on nearby robots. Both principles are implemented in a testbed in which operators exert influence on a robot swarm by switching between a set of behaviors ranging from trivial behaviors up to distributed autonomous algorithms. Performance is tested in a series of complex foraging tasks in environments with different obstacles ranging from open to cluttered and structured. The robotic swarm has only local communication and sensing capabilities with the number of robots ranging from 50 to 200. Experiments with human operators utilizing either selection or beacon control are compared with each other and to a simple autonomous swarm with regard to performance, adaptation to complex environments, and scalability to larger swarms. Our results show superior performance of autonomous swarms in open environments, of selection control in complex environments, and indicate a potential for scaling beacon control to larger swarms.

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Section: Related Workmentioning

confidence: 99%

Towards human control of robot swarms

Kolling

Nunnally

Lewis

2012

Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction

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“…The applications in this area range from autonomously achieving a safe formation of unmanned aerial vehicles [19] to autonomously executing a team-based strategy of robots playing soccer [59] (see Figure 1) to autonomously operating a large (high-traffic) warehouse [30]. One specific application within this range is cooperative adaptive cruise control (CACC).…”

Section: Networked Control Application Areasmentioning

confidence: 99%

Exploring the Boundaries of Robust Stability Under Uncertain Communication: An NCS Toolbox Applied to a Wireless Control Setup

et al. 2014

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“…This concept proposes yet another architecture based on a set of desirable features and presents a simple test system in which operators successfully aid a swarm in locating radiation sources. Also focused on a particular task, a team of Unmanned Aerial Vehicles (UAVs) was controlled by a single operator using behavior-based controls in Ding, Powers, Egerstedt, Young, and Balch (2009). These controls enable UAVs to perform a surveillance mission semi-autonomously while the operator generates a mission plan.…”

Section: Related Workmentioning

confidence: 99%

Human Swarm Interaction: An Experimental Study of Two Types of Interaction with Foraging Swarms

Kolling

Sycara

Nunnally

et al. 2013

Journal of Human-Robot Interaction

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In this paper we present the first study of human-swarm interaction comparing two fundamental types of interaction, coined intermittent and environmental. These types are exemplified by two control methods, selection and beacon control, made available to a human operator to control a foraging swarm of robots. Selection and beacon control differ with respect to their temporal and spatial influence on the swarm and enable an operator to generate different strategies from the basic behaviors of the swarm. Selection control requires an active selection of groups of robots while beacon control exerts an influence on nearby robots within a set range. Both control methods are implemented in a testbed in which operators solve an information foraging problem by utilizing a set of swarm behaviors. The robotic swarm has only local communication and sensing capabilities. The number of robots in the swarm range from 50 to 200. Operator performance for each control method is compared in a series of missions in different environments with no obstacles up to cluttered and structured obstacles. In addition, performance is compared to simple and advanced autonomous swarms. Thirty-two participants were recruited for participation in the study. Autonomous swarm algorithms were tested in repeated simulations. Our results showed that selection control scales better to larger swarms and generally outperforms beacon control. Operators utilized different swarm behaviors with different frequency across control methods, suggesting an adaptation to different strategies induced by choice of control method. Simple autonomous swarms outperformed human operators in open environments, but operators adapted better to complex environments with obstacles. Human controlled swarms fell short of task-specific benchmarks under all conditions. Our results reinforce the importance of understanding and choosing appropriate types of human-swarm interaction when designing swarm systems, in addition to choosing appropriate swarm behaviors.

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Executive decision support

Cited by 33 publications

References 9 publications

Towards human control of robot swarms

Towards human control of robot swarms

Exploring the Boundaries of Robust Stability Under Uncertain Communication: An NCS Toolbox Applied to a Wireless Control Setup

Human Swarm Interaction: An Experimental Study of Two Types of Interaction with Foraging Swarms

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