An adaptive genetic algorithm for the optimization of multi-mobile robot collaboration

Abstract

The proposed approach utilizes the Robot Operating System (ROS) to simulate multi-robot collaboration across various scenarios, ensuring rigorous testing and validation of the algorithm. Our simulation environment encompasses complex tasks such as 3D digitalization, which demand precise coordination and efficient resource management among robots. The adaptive genetic algorithm (GA) continuously adjusts its parameters to improve performance, making it highly suitable for dynamic and unpredictable environments. Our results demonstrate that the adaptive GA significantly enhances the efficiency and effectiveness of Multi-Robot Task Allocation (MRTA) compared to traditional methods that lack optimization. By incorporating a cost function with various weighted factors, the task allocation process becomes both comprehensive and adaptable to specific mission requirements. This ensures that the robots can allocate tasks efficiently, even as conditions change. This study underscores the potential of adaptive genetic algorithms to advance the capabilities of mobile multi-robot systems, particularly in applications that require high levels of collaboration and precision. Our approach not only improves task allocation efficiency but also enhances the overall coordination and performance of the robotic system. The adaptability and robustness of the GA make it a promising solution for real-world applications, including search-and-rescue missions, environmental monitoring, and industrial automation. So, the potential of adaptive genetic algorithm presents a significant advancement in optimizing mobile multi-robot collaboration. Also, its ability to dynamically adjust to changing conditions and improve task allocation processes highlights its potential for a wide range of applications, marking a notable step forward in the field of collaborative robotics.