Bayes Bots: Collective Bayesian Decision-Making in Decentralized Robot Swarms

Ebert, Julia; Gauci, Melvin; Mallmann-Trenn, Frederik; Nagpal, Radhika

doi:10.1109/icra40945.2020.9196584

Cited by 40 publications

(34 citation statements)

References 12 publications

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“…Apart from that, the following two approaches have been proposed for collective perception based on Bayesian statistics. Ebert et al (2020) employed a Bayesian statistics-based collective perception algorithm. In order to obtain the color in majority in their algorithm, each robot is programmed to compute the probability of the fill ratio to be larger than 0.5.…”

Section: Problem Statement and Related Workmentioning

confidence: 99%

“…At the end of State B, if an obstacle is detected, the robot repeats State B with a reset timer. It has been observed in the related literature which used Bayesian statistics in collective perception (Ebert et al 2020;Shan and Mostaghim 2020) that collecting correlated…”

Section: Low-level Control Mechanismmentioning

confidence: 99%

“…Bartashevich and Mostaghim (2019) have investigated collective perception scenarios with different patterns of black and white tiles and their impact on the performances of collective decision-making strategies. Ebert et al (2020) have employed a Bayesian statistics-based approach to perform collective perception on Kilobots. So far, most investigations of collective perception scenarios have been limited to the task of determining the color in majority, thus limiting the number of options for agents to only 2.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Discrete collective estimation in swarm robotics with distributed Bayesian belief sharing

Shan

Mostaghim

2021

Swarm Intell

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Multi-option collective decision-making is a challenging task in the context of swarm intelligence. In this paper, we extend the problem of collective perception from simple binary decision-making of choosing the color in majority to estimating the most likely fill ratio from a series of discrete fill ratio hypotheses. We have applied direct comparison (DC) and direct modulation of voter-based decisions (DMVD) to this scenario to observe their performances in a discrete collective estimation problem. We have also compared their performances against an Individual Exploration baseline. Additionally, we propose a novel collective decision-making strategy called distributed Bayesian belief sharing (DBBS) and apply it to the above discrete collective estimation problem. In the experiments, we explore the performances of considered collective decision-making algorithms in various parameter settings to determine the trade-off among accuracy, speed, message transfer and reliability in the decision-making process. Our results show that both DC and DMVD outperform the Individual Exploration baseline, but both algorithms exhibit different trade-offs with respect to accuracy and decision speed. On the other hand, DBBS exceeds the performances of all other considered algorithms in all four metrics, at the cost of higher communication complexity.

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Section: Problem Statement and Related Workmentioning

confidence: 99%

Section: Low-level Control Mechanismmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Discrete collective estimation in swarm robotics with distributed Bayesian belief sharing

Shan

Mostaghim

2021

Swarm Intell

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“…Contrary to [36], the authors in [4] find that the difficulty of the collective perception process doesn't depend mainly on the ratio of one color to the other, but on the distribution of each color in the environment. The authors in [7] proposed a distributed Bayesian algorithm to solve the collective perception task of a similar two-color environment. They define the speed vs. accuracy trade-off of the collective perception as a multi-objective optimization problem.…”

Section: Related Workmentioning

confidence: 99%

Construction Task Allocation Through the Collective Perception of a Dynamic Environment

Khaluf

Allwright

Rausch

et al. 2020

Lecture Notes in Computer Science

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Building structures is a remarkable collective process but its automation remains an open challenge. Robot swarms provide a promising solution to this challenge. However, collective construction involves a number of difficulties regarding efficient robots allocation to the different activities, particularly if the goal is to reach an optimal construction rate. In this paper, we study an abstract construction scenario, where a swarm of robots is engaged in a collective perception process to estimate the density of building blocks around a construction site. The goal of this perception process is to maintain a minimum density of blocks available to the robots for construction. To maintain this density, the allocation of robots to the foraging task needs to be adjusted such that enough blocks are retrieved. Our results show a robust collective perception that enables the swarm to maintain a minimum block density under different rates of construction and foraging. Our approach leads the system to stabilize around a state in which the robots allocation allows the swarm to maintain a tile density that is close to or above the target minimum.

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“…In comparison with other statistical methods, e.g. Bayesian inference (Ebert et al 2020), DST allows one to combine evidence from different sources without any prior knowledge of their distributions.…”

Section: Introductionmentioning

confidence: 99%

Multi-featured collective perception with Evidence Theory: tackling spatial correlations

Bartashevich

Mostaghim

2021

Swarm Intell

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Collective perception allows sparsely distributed agents to form a global view on a common spatially distributed problem without any direct access to global knowledge and only based on a combination of locally perceived information. However, the evidence gathered from the environment is often subject to spatial correlations and depends on the movements of the agents. The latter is not always easy to control and the main question is how to share and to combine the estimated information to achieve the most precise global estimate in the least possible time. The current article aims at answering this question with the help of evidence theory, also known as Dempster–Shafer theory, applied to the collective perception scenario as a collective decision-making problem. We study eight most common belief combination operators to address the arising conflict between different sources of evidence in a highly dynamic multi-agent setting, driven by modulation of positive feedback. In comparison with existing approaches, such as voter models, the presented framework operates on quantitative belief assignments of the agents based on the observation time of the options according to the agents’ opinions. The evaluated results on an extended benchmark set for multiple options ($$n>2$$ n > 2 ) indicate that the proportional conflict redistribution (PCR) principle allows a collective of small size ($$N=20$$ N = 20 ), occupying $$3.5\%$$ 3.5 % of the surface, to successfully resolve the conflict between clustered areas of features and reach a consensus with almost $$100\%$$ 100 % certainty up to $$n=5$$ n = 5 .

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Bayes Bots: Collective Bayesian Decision-Making in Decentralized Robot Swarms

Cited by 40 publications

References 12 publications

Discrete collective estimation in swarm robotics with distributed Bayesian belief sharing

Discrete collective estimation in swarm robotics with distributed Bayesian belief sharing

Construction Task Allocation Through the Collective Perception of a Dynamic Environment

Multi-featured collective perception with Evidence Theory: tackling spatial correlations

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