Cooperative control and communication of intelligent swarms: a survey

Abstract

Individuals exchange information, experience and strategy based on communication. Communication is the basis for individuals to form swarms and the bridge of swarms to realize cooperative control. In this paper, the multi-robot swarm and its cooperative control and communication methods are reviewed, and we summarize these methods from the task, control, and perception levels. Based on the research, the cooperative control and communication methods of intelligent swarms are divided into the following four categories: task assignment based methods (divided into market-based methods and alliance based methods), bio-inspired methods (divided into biochemical information inspired methods, vision based methods and self-organization based methods), distributed sensor fusion and reinforcement learning based methods, and we briefly define each method and introduce its basic ideas. Based on WOS database, we divide the development of each method into several stages according to the time distribution of the literature, and outline the main research content of each stage. Finally, we discuss the communication problems of intelligent swarms and the key issues, challenges and future work of each method.

Research on a Good Life and Coordinated Development Based on Intelligent Communication and k-Means Algorithm

“…After many experiments, for the data set, the traditional clustering algorithm will appear multiple times in the data results, we might as well use it to express the performance of traditional k -means [ 20 ]. However, the performance of the k -means plus algorithm is not only stable, but sometimes better than k -means; the advantage will be basically the same as k -means, and there is only a small probability that it will be inferior to the traditional k -means algorithm [ 21 ]. Here is a preliminary experimental probability value, which is defined as follows: …”

AI-enhanced biomedical micro/nanorobots in microfluidics

“…The resource allocation with minimized redundancy upholds the systematic efficiency and sustainability. 147 AI-enhanced swarm intelligence promotes continuous improvement in coordinating and collaborating MNRs. By leveraging AI, adaptive decision-making ability based on environment dynamics requirements can be obtained.…”

“…The resource allocation with minimized redundancy upholds the systematic efficiency and sustainability. 147…”

A Deep Q -Network-Based Collaborative Control Research for Smart Ammunition Formation

“…According to Equation (11), after obtaining the maximum Q-function, we need to derive the optimal policy. Using the recursive mechanism, the Q-function can be updated as [36]…”

“…In this literature, it showed that different approach combinations would provide the researcher with suitable knowledge regarding both the benefits and deficiencies. Kun et al [11] divided the collaborative control and communication methods of intelligent swarms into the following four categories: task assignment-based methods, bioinspired methods, distributed sensor fusion, and reinforcement learning-based methods. Based on the basic ideas and introduction of different methods, the future development stages of intelligent group cooperative control and communication are forecasted, and the problems and challenges are put forward.…”