Robot Chain Based Self-organizing Search Method of Swarm Robotics

Luo, Yandong; Guo, Jianwen; Zeng, Zhibin; Chen, Chengzhi; Li, Xiaoyan; Wu, Jiapeng

doi:10.1007/978-3-319-95930-6_14

Cited by 3 publications

(5 citation statements)

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“…Meanwhile, we also used the same experimental parameters to compare our algorithm with previous algorithm. 10 The results are shown in Figure 11. It is clear that the algorithm proposed in this article greatly improves the range and success rate of the robotic target search task.…”

Section: Experiments In An Unbounded Environmentmentioning

confidence: 99%

“…Moreover, we design two different robot motion mechanisms so that the proposed method can be applied in both bounded and unbounded environments. The proposed method is validated in simulation tests in such bounded and unbounded environments, and its performance is compared with that of the robot chain-based self-organizing search method 10 and path formation method in a swarm (PFIAS). 11 The test results show that the proposed method achieves higher efficiency and a better success rate than the PFIAS algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Toward target search approach of swarm robotics in limited communication environment based on robot chains with elimination mechanism

Luo

Wang

et al. 2020

International Journal of Advanced Robotic Systems

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Swarm robotics refers to artificial swarm systems composed of a large number of autonomous mobile robots with relatively simple structures and functions. One of the basic problems of swarm robotics involves the target search process, which entails a cooperative search using limited perception and local interaction of robots under a self-organizing mechanism. When communication is limited, the connectivity of swarm robotics may decrease, leading to the failure of the target search task. This article describes a new target search method based on the robot chain model and the elimination mechanism. The proposed method allows the target search task to be completed efficiently and reliably while maintaining the connectivity of the robots. Experimental results show that the proposed algorithm offers better performance than conventional techniques in terms of search speed and success rate, and provides an effective method for solving the target search problem using swarm robotics in limited-communication environments.

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Section: Experiments In An Unbounded Environmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Toward target search approach of swarm robotics in limited communication environment based on robot chains with elimination mechanism

Luo

Wang

et al. 2020

International Journal of Advanced Robotic Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…(2) Inspired by the behaviour induced by pheromones released by ant colonies, landmark-based navigation and path integration have also been exploited [13,20,21]. Each robot forms a communication network that extends in the environment, always keeping the connection with other robots.…”

Section: Swarm Roboticsmentioning

confidence: 99%

“…Multiple Robot_Nodes will form a network similar to that of Physarum polycephalum. Our previous work has also proven that such a network constructed by multiple robots provides an effective means to address the exploration problem for multiple robots with extremely simple functions and a limited communication range [13,21]. Robot_Explorer: during the foraging process of Physarum polycephalum, the frequency and amplitude of the periodic contractions of the tube walls will increase when the organism encounters an attractant 4 Complexity (such as food), which attracts the protoplasm, and the protoplasm will engulf and digest newly encountered food sources [38].…”

Section: Behavioural Statesmentioning

confidence: 99%

“…However, collective central place foraging by superorganismal social insect colonies elegantly and scalably solves the problem of resource collection in an uncertain environment [10,11]. Accordingly, engineers have drawn inspiration from social insects to design swarm robotics systems that collectively solve foraging-like tasks in a parallel mode [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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Swarm Robot Exploration Strategy for Path Formation Tasks Inspired by Physarum polycephalum

et al. 2021

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Physarum polycephalum, a unicellular and multiheaded slime mould, can form highly efficient networks connecting separated food sources during the process of foraging. These adaptive networks exhibit a unique characteristic in that they are optimized without the control of a central consciousness. Inspired by this phenomenon, we present an efficient exploration and navigation strategy for a swarm of robots, which exploits cooperation and self-organisation to overcome the limited abilities of the individual robots. The task faced by the robots consists in the exploration of an unknown environment in order to find a path between two distant target areas. For the proposed algorithm (EAIPP), we experimentally present robustness tests and obstacle tests conducted to analyse the performance of our algorithm and compare the proposed algorithm with other swarm robot foraging algorithms that also focus on the path formation task. This work has certain significance for the research of swarm robots and Physarum polycephalum. For the research of swarm robotics, our algorithm not only can lead multirobot as a whole to overcome the limitations of very simple individual agents but also can offer better performance in terms of search efficiency and success rate. For the research of Physarum polycephalum, this work is the first one combining swarm robots and Physarum polycephalum. It also reveals the potential of the Physarum polycephalum foraging principle in multirobot systems.

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