Target Enclosure for Multiple Targets

Kubo, Michinori; Satō, Hiroshi; Yamaguchi, Akihiro; Yoshimura, Tatsuro

doi:10.1007/978-3-642-33932-5_75

Cited by 4 publications

(4 citation statements)

References 9 publications

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“…In [12], a method for path planning with obstacle avoidance was presented for micro-robotic drug delivery systems, where it is assumed that the position of the targeted cells is known to the micro-robots. Kubo et al [13] defined swarm based task allocation method to trap targets whose global positions are not available for individual robots.…”

Section: B Backgroundmentioning

confidence: 99%

Regulated Morphogen Gradients for Target Surrounding and Adaptive Shape Formation

Shirazi

Jin

2021

IEEE Trans. Cogn. Dev. Syst.

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In swarm robotics, developing algorithms for self-organizing minimalistic robots has become a popular research topic. Unlike others, minimalistic robots may not be able to self-localize themselves, making it very challenging to accomplish missions such as surrounding a target, whose position is typically unknown. In target surrounding, reaching a target and joining the swarm do not always lead to a satisfactory enclosure of the target. Furthermore, it is impossible for individual minimalistic robots to figure out what a global shape of the swarm should be without a collective decision-making. In this research, we make use of diffusion and reaction of two morphogens for target surrounding and formation of a circular shape swarm. We show that the proposed method is able to adaptively form shapes surrounding multiple targets. Computer simulations and physical experiments using Kilobots are performed to assess the performance of the proposed algorithm.

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Section: B Backgroundmentioning

confidence: 99%

Regulated Morphogen Gradients for Target Surrounding and Adaptive Shape Formation

Shirazi

Jin

2021

IEEE Trans. Cogn. Dev. Syst.

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“…where N rob and N obs are the quantity of robots and obstacles, respectively. fu k;x ; u k;y g is the position of obstacle k, i;j , and g i;j equal to 0 or 1, which is determined by the distance between a robot i and j À th robot and k À th obstacle i;j ¼ 1 kg i À g j k ; i; j 2 f1; 2; :::; N rob g 0 otherwise (9) g i;j ¼ 1 kg i À g j k þ ; k 2 f1; 2; :::; N obs g 0 otherwise (10) where is the perceived radius of robot i. g is the influence range of k À th obstacle. D j i;x ; D j i;y ; O k i;x ; and O k i;y can be calculated as follows…”

Section: Grn-based Controller With Individual Adaptationmentioning

confidence: 99%

“…Pimenta et al 9 proposed an algorithm based on a smoothed particle hydrodynamics technique to trap static targets with environmental constraints. Kubo et al 10 introduced a swarm-based task assignment capability to robots' enclosure model without any global predefined position assignments for each target. Ghanaatpishe et al 11 proposed a stochastic control method for the problem of following, surrounding, and hunting an escaping target.…”

Section: Introductionmentioning

confidence: 99%

Multi-target trapping in constrained environments using gene regulatory network-based pattern formation

Peng

Zhang

Lei

2016

International Journal of Advanced Robotic Systems

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Inspired by the morphogenesis of biological organisms, gene regulatory network-based methods have been used in complex pattern formation of swarm robotic systems. In this article, obstacle information was embedded into the gene regulatory network model to make the robots trap targets with an expected pattern while avoiding obstacles in a distributed manner. Based on the modified gene regulatory network model, an implicit function method was adopted to represent the expected pattern which is easily adjusted by adding extra feature points. Considering environmental constraints (e.g. tunnels or gaps in which robots must adjust their pattern to conduct trapping task), a pattern adaptation strategy was proposed for the pattern modeler to adaptively adjust the expected pattern. Also to trap multiple targets, a splitting pattern adaptation strategy was proposed for diffusively moving targets so that the robots can trap each target separately with split sub-patterns. The proposed model and strategies were verified through a set of simulation with complex environmental constraints and non-consensus movements of targets.

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“…To the best of our knowledge, only a handful of efforts have focused on multiple target entrapping. Kubo et al [23] proposed a swarm robot multi-target entrapping algorithm. However, the multiple targets are stationary.…”

Section: Introductionmentioning

confidence: 99%

AGENT: An Adaptive Grouping Entrapping Method of Flocking Systems

Wang¹,

Gu²,

Kuang³

et al. 2022

Preprint

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This study proposes a distributed algorithm that makes agents' adaptive grouping entrap multiple targets via automatic decision making, smooth flocking, and well-distributed entrapping. Agents make their own decisions about which targets to surround based on environmental information. An improved artificial potential field method is proposed to enable agents to smoothly and naturally change the formation to adapt to the environment. The proposed strategies guarantee that the coordination of swarm agents develops the phenomenon of multiple targets entrapping at the swarm level. We validate the performance of the proposed method using simulation experiments and design indicators for the analysis of these simulation and physical experiments.

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Target Enclosure for Multiple Targets

Cited by 4 publications

References 9 publications

Regulated Morphogen Gradients for Target Surrounding and Adaptive Shape Formation

Regulated Morphogen Gradients for Target Surrounding and Adaptive Shape Formation

Multi-target trapping in constrained environments using gene regulatory network-based pattern formation

AGENT: An Adaptive Grouping Entrapping Method of Flocking Systems

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