Decentralized probabilistic density control of autonomous swarms with safety constraints

Demir, Nazlı; Eren, Utku; Açıkmeşe, Behçet

doi:10.1007/s10514-015-9470-z

Cited by 44 publications

(42 citation statements)

References 49 publications

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“…We also provide a contrast between our method and another common way to measure error, namely, by discretizing the domain (e.g. [4,14]) in Subsection 2.3. Finally, in Subsection 2.4, we present the setup for the two main examples that we will use throughout the paper.…”

Section: Quantifying Coveragementioning

confidence: 99%

“…In many applications of swarm robotics, the swarm must spread across a domain according to a target distribution in order to achieve its goal. Some examples are in surveillance and area coverage [8,20,23,31], achieving a heterogeneous target distribution [4,10,14,16,17,34,41], and aggregation and pattern formation [28][29][30][36][37][38]. Despite the importance of assessing performance, some studies such as [28,29,34,38,41] rely only on qualitative methods such as visual comparison.…”

Section: Introductionmentioning

confidence: 99%

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Quantifying Robotic Swarm Coverage

Anderson

Loeser

Gee

et al. 2019

Informatics in Control, Automation and Robotics

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In the field of swarm robotics, the design and implementation of spatial density control laws has received much attention, with less emphasis being placed on performance evaluation. This work fills that gap by introducing an error metric that provides a quantitative measure of coverage for use with any control scheme. The proposed error metric is continuously sensitive to changes in the swarm distribution, unlike commonly used discretization methods. We analyze the theoretical and computational properties of the error metric and propose two benchmarks to which error metric values can be compared. The first uses the realizable extrema of the error metric to compute the relative error of an observed swarm distribution. We also show that the error metric extrema can be used to help choose the swarm size and effective radius of each robot required to achieve a desired level of coverage. The second benchmark compares the observed distribution of error metric values to the probability density function of the error metric when robot positions are randomly sampled from the target distribution. We demonstrate the utility of this benchmark in assessing the performance of stochastic control algorithms. We prove that the error metric obeys a central limit theorem, develop a streamlined method for performing computations, and place the standard statistical tests used here on a firm theoretical footing. We provide rigorous theoretical development, computational methodologies, numerical examples, and MATLAB code for both benchmarks.

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Section: Quantifying Coveragementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantifying Robotic Swarm Coverage

Anderson

Loeser

Gee

et al. 2019

Informatics in Control, Automation and Robotics

View full text Add to dashboard Cite

show abstract

“…This section presents necessary definitions and assumptions for our proposed framework, which will be shown in Section III. Since most of them are embraced from the recent existing literature [10], [13], we here briefly provide their essential meanings.…”

Section: A Definitionsmentioning

confidence: 99%

“…Assumption 2 (The number of agents [6], [10], [13], [15], [17]). It is assumed that n A k n bin so that the time evolution of the swarm distribution is governed by the stochastic decision policy in Equation (3).…”

Section: A Definitionsmentioning

confidence: 99%

Local information-based control for probabilistic swarm distribution guidance

2018

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This paper addresses a task allocation problem for a large-scale robotic swarm, namely swarm distribution guidance problem. Unlike most of the existing frameworks handling this problem, the proposed framework suggests utilising local information available to generate its time-varying stochastic policies. As each agent requires only local consistency on information with neighbouring agents, rather than the global consistency, the proposed framework offers various advantages, e.g., a shorter timescale for using new information and potential to incorporate an asynchronous decision-making process. We perform theoretical analysis on the properties of the proposed framework. From the analysis, it is proved that the framework can guarantee the convergence to the desired density distribution even using local information while maintaining advantages of global-informationbased approaches. The design requirements for these advantages are explicitly listed in this paper. This paper also provides specific examples of how to implement the framework developed. The results of numerical experiments confirm the effectiveness and comparability of the proposed framework, compared with the global-information-based framework.

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“…However, they still face congestion problems near the common targets when the number of robots is large, decreasing performance. In a very recent work, Demir et al (2015) study how to use Markov chains to control a swarm to a certain desired spatial distribution, but such approach does not apply for the common target problem studied in this paper.…”

Section: Related Workmentioning

confidence: 99%

Avoiding target congestion on the navigation of robotic swarms

et al. 2016

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Robotic swarms are decentralized systems formed by a large number of robots. A common problem encountered in a swarm is congestion, as a great number of robots often must move towards the same region. This happens when robots have a common target, for example during foraging or waypoint navigation. We propose three algorithms to alleviate congestion: in the first, some robots stop moving towards the target for a random number of iterations; in the second, we divide the scenario in two regions: one for the robots that are moving towards the target, and another for the robots that are leaving the target; in the third, we combine the two previous algorithms. We evaluate our algorithms in simulation, where we show that all of them effectively improve navigation. Moreover, we perform an experimental analysis in the real world with ten robots, and show that all our approaches improve navigation with statistical significance.

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Decentralized probabilistic density control of autonomous swarms with safety constraints

Cited by 44 publications

References 49 publications

Quantifying Robotic Swarm Coverage

Quantifying Robotic Swarm Coverage

Local information-based control for probabilistic swarm distribution guidance

Avoiding target congestion on the navigation of robotic swarms

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