Swarm Robotics: Past, Present, and Future [Point of View]

Abstract

S warm robotics deals with the design, construction, and deployment of large groups of robots that coordinate and cooperatively solve a problem or perform a task. It takes inspiration from natural self-organizing systems, such as social insects, fish schools, or bird flocks, characterized by emergent collective behavior based on simple local interaction rules [1], [2]. Typically, swarm robotics extracts engineering principles from the study of those natural systems in order to provide multirobot systems with comparable abilities. This way, it aims to build systems that are more robust, fault-tolerant, and flexible than single robots and that can better adapt their behavior to changes in the environment.

Characterization of Indicators for Adaptive Human-Swarm Teaming

“…In addition, as discussed in Section 9 , the functionality of the adaptive control agent can be implemented through multiple ways, including a set of rules, fuzzy logic, reinforcement learning and other machine learning techniques. Among these methods, rule-based or decision tree-based methods have advantages in improving the transparency and explainability of decisions made by the adaptive control agent ( Dorigo et al, 2021 ). Methods based on deep learning algorithms, on the other hand, are able to make full use of the available data and have the potential in providing more effective and robust representations for adaptive control ( Benosman, 2018 ).…”

Social learning in swarm robotics

“…not limited to learning. The interested reader is referred to [ 4 , 47 , 48 ] for recent discussions on hardware, software or operational issues raised in swarm robotics.…”

Generalized pigeon-inspired optimization algorithm for balancing exploration and exploitation

“…广义群体智能指基于多个简单个体组成的群体, 利用群体间的协作、竞争、认知与学习模式, 完成对 复杂问题的学习和优化求解. 群体智能包括四个方 面的内容：基于群体智能现象的仿真和建模(swarm simulation) [8] [9] 和基于群体智能的应用, 如群体机器人 [10] 、基于群体智能的协同通讯模型和协同感知技术 [11] 等.…”

“…群体优化算法是群体智能中热门的研究领域, 以粒子群优化算法和蚁群优化算法为起始, 基于物 理学现象、 生物学现象和人类群体行为的大量新算法 被 提 出 , 其 中 代 表 性 算 法 包 括 烟 花 算 法 (fireworks algorithm) [7] 、 头 脑 风 暴 优 化 算 法 (brain storm optimization) [12,13] 、鸽群优化算法 [2,14] 等. 群体智能方 法获得了广泛的研究和应用, 如基于群体智能的协 同通讯模型和协同感知技术 [11] 、 群体机器人的参数控 制模型优化 [15] 、多机器人室内环境地图构建 [16] 等.…”