Plasticity in Collective Decision-Making for Robots: Creating Global Reference Frames, Detecting Dynamic Environments, and Preventing Lock-ins

Soorati, Mohammad Divband; Krome, Maximilian; Mora-Mendoza, Marco Antonio; Ghofrani, Javad; Hamann, Heiko

doi:10.1109/iros40897.2019.8967777

Cited by 11 publications

(8 citation statements)

References 19 publications

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“…There were other minimal behaviors, e.g., (19), that could potentially be modified to remove limiting assumptions on a robot's prior knowledge; however, it has been shown that they were unable to adapt to environmental changes (19,20). The few studies on best-of-n decisions in time-varying environments were limited to n = 2 binary decisions with robots knowing a priori the options (21) or their location (20,22) and only agreeing on the one with the highest quality. Requiring prior knowledge about the number of alternatives n may reduce the applicability of such solutions.…”

Section: Introductionmentioning

confidence: 99%

When less is more: Robot swarms adapt better to changes with constrained communication

et al. 2021

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To effectively perform collective monitoring of dynamic environments, a robot swarm needs to adapt to changes by processing the latest information and discarding outdated beliefs. We show that in a swarm composed of robots relying on local sensing, adaptation is better achieved if the robots have a shorter rather than longer communication range. This result is in contrast with the widespread belief that more communication links always improve the information exchange on a network. We tasked robots with reaching agreement on the best option currently available in their operating environment. We propose a variety of behaviors composed of reactive rules to process environmental and social information. Our study focuses on simple behaviors based on the voter model—a well-known minimal protocol to regulate social interactions—that can be implemented in minimalistic machines. Although different from each other, all behaviors confirm the general result: The ability of the swarm to adapt improves when robots have fewer communication links. The average number of links per robot reduces when the individual communication range or the robot density decreases. The analysis of the swarm dynamics via mean-field models suggests that our results generalize to other systems based on the voter model. Model predictions are confirmed by results of multiagent simulations and experiments with 50 Kilobot robots. Limiting the communication to a local neighborhood is a cheap decentralized solution to allow robot swarms to adapt to previously unknown information that is locally observed by a minority of the robots.

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Section: Introductionmentioning

confidence: 99%

When less is more: Robot swarms adapt better to changes with constrained communication

et al. 2021

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“…The swarm can establish a distributed sensing on a large area of the environment and when working together, the swarm will be able to construct an opinion about a certain property of the environment. In our earlier work, we have shown how a swarm can collectively decide which half of an environment is brighter and stay adaptive to changes [22]. This class of problems in swarm robotics is called the best-of-n problem, that is, the capability of the swarm to find the best option among a finite set of alternatives [23].…”

Section: Human-swarm Collective Decision-making Modelmentioning

confidence: 99%

“…To the best of our knowledge, none of the existing collective decision-making strategies including Direct Modulation of Voter-based Decisions [26], Direct Modulation of Majority-based Decisions [27], Direct Comparison [25] that are widely common in the swarm robotics applications can be directly applied to our scenario, as UAVs need to continuously measure the environment and disseminate whenever another UAV or an operator is in range. We are building upon our previous work on adaptive decision-making in robot swarms and add a human in the loop to find a choice among a large set of options in a dynamic environment [22].…”

Section: Human-swarm Collective Decision-making Modelmentioning

confidence: 99%

Designing a User-Centered Interaction Interface for Human–Swarm Teaming

Soorati

Clark

Ghofrani

et al. 2021

Drones

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A key challenge in human–swarm interaction is to design a usable interface that allows the human operators to monitor and control a scalable swarm. In our study, we restrict the interactions to only one-to-one communications in local neighborhoods between UAV-UAV and operator-UAV. This type of proximal interactions will decrease the cognitive complexity of the human–swarm interaction to O(1). In this paper, a user study with 100 participants provides evidence that visualizing a swarm as a heat map is more effective in addressing usability and acceptance in human–swarm interaction. We designed an interactive interface based on the users’ preference and proposed a controlling mechanism that allows a human operator to control a large swarm of UAVs. We evaluated the proposed interaction interface with a complementary user study. Our testbed and results establish a benchmark to study human–swarm interaction where a scalable swarm can be managed by a single operator.

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“…Other recent works in this field have focused on the swarm sensing of a single binary state in dynamic environments, exploring under which conditions communication rules enable a collective to follow changes in the environment [21][22][23][24]. In these models, individuals can either individually determine the state of the environment on their own or make up their minds by aggregating group opinions.…”

Section: Introductionmentioning

confidence: 99%

Sensing Enhancement on Social Networks: The Role of Network Topology

Brede

Moreno

2022

Entropy

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Sensing and processing information from dynamically changing environments is essential for the survival of animal collectives and the functioning of human society. In this context, previous work has shown that communication between networked agents with some preference towards adopting the majority opinion can enhance the quality of error-prone individual sensing from dynamic environments. In this paper, we compare the potential of different types of complex networks for such sensing enhancement. Numerical simulations on complex networks are complemented by a mean-field approach for limited connectivity that captures essential trends in dependencies. Our results show that, whilst bestowing advantages on a small group of agents, degree heterogeneity tends to impede overall sensing enhancement. In contrast, clustering and spatial structure play a more nuanced role depending on overall connectivity. We find that ring graphs exhibit superior enhancement for large connectivity and that random graphs outperform for small connectivity. Further exploring the role of clustering and path lengths in small-world models, we find that sensing enhancement tends to be boosted in the small-world regime.

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Plasticity in Collective Decision-Making for Robots: Creating Global Reference Frames, Detecting Dynamic Environments, and Preventing Lock-ins

Cited by 11 publications

References 19 publications

When less is more: Robot swarms adapt better to changes with constrained communication

When less is more: Robot swarms adapt better to changes with constrained communication

Designing a User-Centered Interaction Interface for Human–Swarm Teaming

Sensing Enhancement on Social Networks: The Role of Network Topology

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