Multi-Robot Coordination in Dynamic Environments Shared with Humans

Talebpour, Zeynab; Martinoli, Alcherio

doi:10.1109/icra.2018.8460978

Cited by 12 publications

(10 citation statements)

References 18 publications

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“…Moreover, human–machine interfaces are becoming more and more relevant in several environments, such as hospitals, restaurants, and service areas. In HRI, smart robots are supposed to respond to well-established human gestures [ 128 , 129 ]. At the same time, corresponding data processing aims to reach the maximum autonomy and safety in HRI.…”

Section: Issues and Challengesmentioning

confidence: 99%

Internet of Robotic Things in Smart Domains: Applications and Challenges

Romeo

Petitti

Marani

et al. 2020

Sensors

110

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With the advent of the Fourth Industrial Revolution, Internet of Things (IoT) and robotic systems are closely cooperating, reshaping their relations and managing to develop new-generation devices. Such disruptive technology corresponds to the backbone of the so-called Industry 4.0. The integration of robotic agents and IoT leads to the concept of the Internet of Robotic Things, in which innovation in digital systems is drawing new possibilities in both industrial and research fields, covering several domains such as manufacturing, agriculture, health, surveillance, and education, to name but a few. In this manuscript, the state-of-the-art of IoRT applications is outlined, aiming to mark their impact on several research fields, and focusing on the main open challenges of the integration of robotic technologies into smart spaces. IoRT technologies and applications are also discussed to underline their influence in everyday life, inducing the need for more research into remote and automated applications.

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Section: Issues and Challengesmentioning

confidence: 99%

Internet of Robotic Things in Smart Domains: Applications and Challenges

Romeo

Petitti

Marani

et al. 2020

Sensors

110

View full text Add to dashboard Cite

show abstract

“…In our previous work, a study of MRTA in dynamic and noisy environments [18] for spatial task allocation confirmed the effectiveness of our Hoplites-based method [19] as a first step towards deployment of MRTA methods in social environments. In [20], we proposed a humanaware coordination method for MRTA that accounted for the distance overhead of human-aware paths in the local balance function of the robots and allowed for instant collaboration in socially blocking situations. However, robots could only see a snapshot of the environment and costs were assumed to be deterministic.…”

Section: Related Workmentioning

confidence: 99%

“…This process is ruled by a market-based approach and constitutes the active coordination. For details about the Hoplites-based MRTA refer to [18] and [20].…”

Section: B Social Multi-robot Coordinationmentioning

confidence: 99%

“…While many formulations can be used for encoding the local balance function of robots, operating in social environments calls for including a social factor in bids. Thus, human-aware bids and social time-outs are used to encode the social information into the coordination framework [20]. Social time-outs are used for requesting collaboration when socially blocking situations occur.…”

Section: B Social Multi-robot Coordinationmentioning

confidence: 99%

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Risk-Based Human-Aware Multi-Robot Coordination in Dynamic Environments Shared with Humans

Talebpour

Martinoli

2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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In this paper, we propose a risk-based coordination method for the Multi-Robot Task Allocation (MRTA) problem in human-populated environments. We introduce riskbased bids that incorporate human trajectory prediction uncertainties and furthermore, social costs in their formulation. We demonstrate the effectiveness of including a predictive component in the risk formulation despite the lack of accurate position estimation for humans through an extensive suite of experiments. This is done by means of testing different levels of prediction error for known human trajectories and in a separate approach, using a Kalman filter for human trajectory estimation. Furthermore, we propose different risk formulations and evaluate their performance in a high-fidelity simulator. Additionally, a comparative study targeting human-agnostic planning at both navigation and planning levels, human-aware navigation and planning based on deterministic costs, and riskbased human-aware planning with no individual human-aware navigation has been conducted. Results confirm that risk-based bids lead to more socially acceptable team plans that reduce the need for the lower level individual human-aware navigation to be activated. Risk-based plans that account for social costs, prevent difficult social situations that can lead to less effective human-aware navigation, such as traversing narrow passages occupied by humans.

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“…We employ a Hoplites-based MRTA framework [7] as the basis of our human-aware MRTA approach. The contributions of this paper include proposing an Adaptive Risk-Based Replanning (ARBR) strategy for handling i) new information and ii) unpredicted human behavior, leveraging the concept of risk-based bid estimation for human-aware coordination introduced in [5] and human motion uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Risk-Based Replanning For Human-Aware Multi-Robot Task Allocation With Local Perception

Talebpour

Martinoli

2019

IEEE Robot. Autom. Lett.

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In this paper, we propose an adaptive risk-based replanning strategy in the context of multi-robot task allocation for dealing with limitations of local perception and unpredicted human behavior. Our replanning method is based on the variations of social risk and human motion prediction uncertainty. The performance of our method is studied through an extensive suite of experiments of increasing complexity. Results obtained using both a high-fidelity simulator and real robots confirm that this strategy outperforms a non-adaptive replanning strategy in all cases with respect to the chosen social metrics. The overall performance of the team depends firstly on its replanning strategy, and secondly on the available information about the humans. Although an adaptive replanning strategy with global perception leads to the best performance, it is computationally expensive and infeasible in some real applications. Local perception shows comparable results as long as updates of relevant human poses affecting a task's risk are available within the execution time of that task. Conversely, the non-adaptive replanning strategy is shown to have degraded results with global perception as decisions in this case can be based on outdated information that lead to invalid plans.

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Multi-Robot Coordination in Dynamic Environments Shared with Humans

Cited by 12 publications

References 18 publications

Internet of Robotic Things in Smart Domains: Applications and Challenges

Internet of Robotic Things in Smart Domains: Applications and Challenges

Risk-Based Human-Aware Multi-Robot Coordination in Dynamic Environments Shared with Humans

Adaptive Risk-Based Replanning For Human-Aware Multi-Robot Task Allocation With Local Perception

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