Swarm & Computational Intelligence Laboratory (SwaCIL), Department of Electrical & Electronic Engineering, The University of Manchester, Manchester M13 9PL, UK.
Bristol Robotic Lab, University of West England, Bristol, Bristol BS16 1QY, UK.
Sensors (Basel). 2020 Jun 10;20(11):3308. doi: 10.3390/s20113308.
Swarm robotics focuses on decentralised control of large numbers of simple robots with limited capabilities. Decentralised control in a swarm system requires a reliable communication link between the individuals that is able to provide linear and angular distances between the individuals-. This study presents the development of an open-source, low-cost communication module which can be attached to miniature sized robots; e.g., . In this study, we only focused on bearing estimation to mathematically model the bearings of neighbouring robots through systematic experiments using real robots. In addition, the model parameters were optimised using a genetic algorithm to provide a reliable and precise model that can be applied for all robots in a swarm. For further investigation and improvement of the system, an additional layer of optimisation on the hardware layout was implemented. The results from the optimisation suggested a new arrangement of the sensors with slight angular displacements on the developed board. The precision of bearing was significantly improved by optimising in both software level and re-arrangement of the sensors' positions on the hardware layout.
群体机器人专注于对大量具有有限能力的简单机器人进行去中心化控制。群体系统中的去中心化控制需要个体之间具有可靠的通信链路,该链路能够提供个体之间的线性和角度距离。本研究提出了一种开源、低成本的通信模块的开发,该模块可以附加到微型机器人上;例如。在本研究中,我们仅专注于方位估计,通过使用真实机器人进行系统实验,从数学上对相邻机器人的方位进行建模。此外,使用遗传算法优化模型参数,提供适用于群体中所有机器人的可靠和精确模型。为了进一步研究和改进系统,在硬件布局上增加了一层优化。优化结果建议对开发板上的传感器进行轻微角度位移的新布置。通过在软件级别和重新布置传感器位置的硬件布局上进行优化,显著提高了方位的精度。