Developing a Bio-Inspired Algorithm for Cooperative Exploration by Swarm Underwater Robots

In this study, a new algorithm is introduced to improve the cooperative exploration function of swarm underwater robots. Thus, based on foraging ants or schooling fish movements, the algorithm wants to enhance the pertinence and flexibility of underwater robotic swarms in chaotic conditions. This algorithm has been created and used in this study and then validated in simulated and underwater actual environments. A blending of python and C++ is used in the algorithm implementation since Python is flexible as opposed to C++ that is fast. It carries provisions for local communication, decentralised decision making and responsiveness to changes in the environment. The performance of the algorithm is assessed using the simulated environment analyzing its performance in different underwater situations, and in the real-world experiments that give a clearer picture of the algorithm’s operations. The outcome of the simulation and experiment proves that it has a better performance in terms of exploration coverage, coordination accuracy and adaptiveness over the current methods. The effectiveness of the proposed algorithm can be seen from the results and compared to the traditional methods in terms of efficiency and robustness and thereby can help in improving the chances of the underwater exploration missions.