A control lyapunov function approach to human-swarm interactions

Croix, Jean-Pierre de la; Egerstedt, Magnus

doi:10.1109/acc.2015.7172016

Cited by 9 publications

(8 citation statements)

References 18 publications

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“…Yet, resulting formal guarantees and analysis tools could still be useful for human operators and system designers. For instance, in [39], formal methods are used to determine whether human control inputs for certain swarm tasks are theoretically possible. Control-theoretic approaches are, therefore, an important complementary contribution to bioinspired works.…”

Section: A Swarm Modelsmentioning

confidence: 99%

Human Interaction With Robot Swarms: A Survey

Kolling

Walker

Chakraborty

et al. 2016

IEEE Trans. Human-Mach. Syst.

332

216

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Abstract-Recent advances in technology are delivering robots of reduced size and cost. A natural outgrowth of these advances are systems comprised of large numbers of robots that collaborate autonomously in diverse applications. Research on effective autonomous control of such systems, commonly called swarms, has increased dramatically in recent years and received attention from many domains, such as bioinspired robotics and control theory. These kinds of distributed systems present novel challenges for the effective integration of human supervisors, operators, and teammates that are only beginning to be addressed. This paper is the first survey of human-swarm interaction (HSI) and identifies the core concepts needed to design a human-swarm system. We first present the basics of swarm robotics. Then, we introduce HSI from the perspective of a human operator by discussing the cognitive complexity of solving tasks with swarm systems. Next, we introduce the interface between swarm and operator and identify challenges and solutions relating to human-swarm communication, state estimation and visualization, and human control of swarms. For the latter, we develop a taxonomy of control methods that enable operators to control swarms effectively. Finally, we synthesize the results to highlight remaining challenges, unanswered questions, and open problems for HSI, as well as how to address them in future works.Index Terms-Human-robot interaction (HRI), human-swarm interaction (HSI), multi-robot systems, swarm robotics.

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Section: A Swarm Modelsmentioning

confidence: 99%

Human Interaction With Robot Swarms: A Survey

Kolling

Walker

Chakraborty

et al. 2016

IEEE Trans. Human-Mach. Syst.

332

216

View full text Add to dashboard Cite

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“…Situation awareness is provided to the operator in terms of an appropriately designed and modulated force feedback, and a general-purpose haptic teleoperation device is exploited. Conversely, (Diana et al 2013) exploits the concept of "deformable affordance", introducing an ad-hoc deformable teleoperation device that is used by the human operator to globally define the shape of the multi-robot system: the stability of the interaction system is discussed in de la Croix and Egerstedt (2015). This approach moves towards the introduction of interaction paradigms that are natural for the humans, and that can then be easily exploited.…”

Section: Interaction With Multi-robot Systemsmentioning

confidence: 99%

Humans interacting with multi-robot systems: a natural affect-based approach

et al. 2019

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This paper proposes a novel human-multi-robot-system interaction approach that enjoys two main features: natural interaction and affect-based adaptation of robots behavior. Specifically, the proposed system enables interaction by means of a wrist-worn device, such as a commercial smartwatch, which allows to track user's movements and heart activity. Thus, on the one side, the proposed system allows the user to intuitively drive the robots by establishing a natural mapping between wrist movements and robots velocity. On the other side, the system estimates user's mental fatigue during interaction by means of the analysis of heart rate variability. The proposed interaction system adapts then the behavior of the multi-robot system when the interacting user gets overwhelmed with the interaction and control task, which is then simplified. Experimental validation is provided, to show the effectiveness of the proposed system. First, the natural and affect-based interaction are considered separately. Then, the approach is tested considering a complex realistic scenario, which is simulated in virtual reality in order to get an immersive and realistic interaction experience. The results of the experimental validation clearly show that the proposed affect-based adaptive system leads to relieving the user's fatigue and mental workload. Keywords Human-multi-robot-system interaction • Natural ineraction • Affective robotics • Human-centred robotics This is one of the several papers published in Autonomous Robots comprising the Special Issue on Multi-Robot and Multi-Agent Systems.

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“…Finally, the Υ in (46) and a sufficiently large damping gain D guarantee that D i ≥ 0 in (40). Algorithm 1 summarizes the gain selection procedure for the control (30), which maintains the tree network of the time-delay EL swarm.…”

Section: Time-delay Slave Swarmmentioning

confidence: 99%

“…Algorithm 1 Gain selection for the dynamic control (30) Input: r, , f , λ i2 , c i , T ij , x d ij ∀j ∈ N i (0) Output: K i (t), B and D 1: Choose σ > 0 and B > 0 heuristically; 2: Select 0 < κ < 1 and Q > 0 such that ω 1 > 0 and ω 2 > 0 in condition (4); 3: Compute ρ i , choose P > 0 and 0 < σ < σ by (41) such that ω 3 > 0 in condition (4); 4: Choose positive η i , γ i and ζ i heuristically; 5: Set Λ ij1 (t) by (43), and update K i (t) dynamically by (47); 6: Compute Λ ij1 , Λ ij2 , K i and Υ by ( 44)- (46), respectively;…”

Section: Time-delay Slave Swarmmentioning

confidence: 99%

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Connectivity-Preserving Swarm Teleoperation With A Tree Network

Yuan

Constantinescu

Shi

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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A teleoperated swarm must follow the unpredictable commands of its human operator while remaining connected. When the swarm communications are limited by distance and affected by delays, both the user input and the transmission delays endanger the connectivity of the swarm. This paper presents a constructive control strategy that overcomes both threats. The strategy modulates the intra-swarm couplings and the damping injected to each slave in the swarm based on a customized potential. Lyapunov-based set invariance analysis proves that the proposed explicit gain updating law limits the impact of the operator input and preserves the initial tree connectivity of a delay-free swarm. Further augmentation with stricter selection of control gains robustifies the design to timevarying delays in intra-swarm communications. The paper also establishes the input-to-state stability of a teleoperated timedelay swarm under the proposed dynamic control. Experiments validate connectivity maintenance and synchronization during time-delay swarm teleoperation with the proposed control. arXiv:1907.07668v1 [eess.SY]

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A control lyapunov function approach to human-swarm interactions

Cited by 9 publications

References 18 publications

Human Interaction With Robot Swarms: A Survey

Human Interaction With Robot Swarms: A Survey

Humans interacting with multi-robot systems: a natural affect-based approach

Connectivity-Preserving Swarm Teleoperation With A Tree Network

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