Swarm Robotics Target Searching Strategy Based on Extended Bat Algorithm
A swarm robotics system can consists of at least two or up to hundreds or thousands number of robots. To build a system that is able to perform target searching task, it needs a robust algoritm and communication strategy. A wrong strategy can lead to unsatisfactory performance in which the swarm robots would unable to move efficiently and arrive at the target position precisely. This work aims to develop a new method for target searching strategy for swarm robotics system by adapting Extended Bat Algorithm (EBA) to the system. EBA is the low level hybrid algorithm of Bat Algorithm (BA) and Spiral Dynamic Algoruthm (SDA), and therefore its exploration and exploitation method is better than BA and SDA. EBA had proven its ability to solve general mathematical problem, however, for swarm robotics system application, its performance and effectiveness still needs to be comprehensively investigated. The investigation result shows that EBA can prove its potentiality to develop the best target searching strategy to the swarm robotics system with 5 number of iterations within 49 seconds. This is found to be the lowest number of iterations in the shortest of time. The accuracy is 99% to arrive at the desired location. Hence, the proposed EBA method is able to perform a target searching task for swarm robotics system.
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