A Bio-Inspired Approach to Task Assignment of Swarm Robots in 3-D Dynamic Environments

Yi, Xin; Zhu, Aibin; Yang, Simon X.; Luo, Chaomin

doi:10.1109/tcyb.2016.2535153

Cited by 57 publications

(19 citation statements)

References 32 publications

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“…Most works on cooperative mobile robots for observing moving targets focus on ground targets moving on 2D plane and assume some prior knowledge about the target's mobility. There is only limited research on targets moving in 3D space available, such as aerial targets [49], [85], [90] or underwater targets [19], [86]. Targets moving on an uneven (ground) surface [91] also exhibit 3D movement.…”

Section: B Targetmentioning

confidence: 99%

Cooperative Robots to Observe Moving Targets: Review

Khan

Rinner

Cavallaro

2018

IEEE Trans. Cybern.

163

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The deployment of multiple robots for achieving a common goal helps to improve the performance, efficiency, and/or robustness in a variety of tasks. In particular, the observation of moving targets is an important multirobot application that still exhibits numerous open challenges, including the effective coordination of the robots. This paper reviews control techniques for cooperative mobile robots monitoring multiple targets. The simultaneous movement of robots and targets makes this problem particularly interesting, and our review systematically addresses this cooperative multirobot problem for the first time. We classify and critically discuss the control techniques: cooperative multirobot observation of multiple moving targets, cooperative search, acquisition, and track, cooperative tracking, and multirobot pursuit evasion. We also identify the five major elements that characterize this problem, namely, the coordination method, the environment, the target, the robot and its sensor(s). These elements are used to systematically analyze the control techniques. The majority of the studied work is based on simulation and laboratory studies, which may not accurately reflect real-world operational conditions. Importantly, while our systematic analysis is focused on multitarget observation, our proposed classification is useful also for related multirobot applications.

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Section: B Targetmentioning

confidence: 99%

Cooperative Robots to Observe Moving Targets: Review

Khan

Rinner

Cavallaro

2018

IEEE Trans. Cybern.

163

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“…There are various techniques for multi-robot task scheduling such as market-based [28,29], bio-inspired [30,31], and hybrid approaches [32]. In the market-based approach, the task is divided into a set of subtasks and the robots bid to carry out these subtasks.…”

Section: Multi-robot Task Schedulingmentioning

confidence: 99%

CPS-Based Smart Warehouse for Industry 4.0: A Survey of the Underlying Technologies

et al. 2018

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This paper discusses how the state-of-the-art techniques in cyber-physical systems facilitate building smart warehouses to achieve the promising vision of industry 4.0. We focus on four significant issues when applying CPS techniques in smart warehouses. First, efficient CPS data collection: when limited communication bandwidth meets numerous CPS devices, we need to make more effort to study efficient wireless communication scheduling strategies. Second, accurate and robust localization: localization is the basis for many fundamental operations in smart warehouses, but still needs to be improved from various aspects like accuracy and robustness. Third, human activity recognition: human activity recognition can be applied in human-computer interaction for remote machine operations. Fourth, multi-robot collaboration: smart robots will take the place of humans to accomplish most tasks particularly in a harsh environment, and smart and fully-distributed robot collaborating algorithms should be investigated. Finally, we point out some challenging issues in the future CPS-based smart warehouse, which could open some new research directions.

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“…A new Bioinspired Neural Network (BNN)-based method was proposed in [7], which combines with a developed vectordriven autonomous robot navigation, and the authors verified that the model can plan more reasonable and shorter collisionfree paths in non-stationary and unstructured environments. The advantages and characteristics of the BNN was integrated with a Self Organizing Map (SOM) to handle task assignment of a swarm of robots in 3D dynamic environment in [8], [9]. In order to improve the BNN performance in dynamic obstacle avoidance task, a dynamic risk level was introduced by [10].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Scale Map Method Based on Bioinspired Neural Network Algorithm for Robot Path Planning

2019

Self Cite

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With the wide application of Bioinspired Neural Network in the field of robot path planning, the environmental scale of robot path planning is getting larger, and the environmental resolution requirements are getting higher. However, with the increase of the environment size and resolution requirement, the neuronal activity value calculation cost and the time cost of the Bioinspired Neural Network will increase sharply. Aiming at this problem, this paper proposes an improved Bioinspired Neural Network path planning method based on Scaling Terrain. Using a Multi-Scale Map method and Dijkstra algorithm, the optimal path of a Coarse Scale Map is calculated. The optimal path obtained from the Coarse Scale Map is used to guide the neural network planning weights of the Fine Scale Map from the same terrain. Thus, the optimal path of the Fine Scale Map can be calculated by the improved BNN algorithm. Introducing this Multi-Scale Map Method into the Bioinspired Neural Network can greatly reduce the time cost of the Bioinspired Neural Network path planning algorithm and reduce the mathematical complexity. Simulation results in some computer integrated virtual environments further demonstrate the superiority of this method and the experimental results are encouraging.INDEX TERMS Multi-scale map method, path planning, bioinspired neural network, Dijkstra algorithm.

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A Bio-Inspired Approach to Task Assignment of Swarm Robots in 3-D Dynamic Environments

Cited by 57 publications

References 32 publications

Cooperative Robots to Observe Moving Targets: Review

Cooperative Robots to Observe Moving Targets: Review

CPS-Based Smart Warehouse for Industry 4.0: A Survey of the Underlying Technologies

A Multi-Scale Map Method Based on Bioinspired Neural Network Algorithm for Robot Path Planning

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