A new cloud autonomous system as a service for multi-mobile robots

Today, mobile robot is used in most industrial and commercial fields. It can improve and carry out work complex tasks quickly and efficiently. However, using swarm robots to execute some tasks requires a complex system for assigning robots to these tasks. The main issue in the robot control systems is the limited facilities of robot embedded system components. Although, some researchers used cloud computing to develop robot services. They didn't use the cloud for solving robot control issues. In this paper, we have used cloud computing for controlling robots to solve the problem of limited robot processing components. The main advantage of using cloud computing is its intensive computing power. This advantage motivates us to propose a new autonomous system for multi-mobile robots as a services-based cloud computing. The proposed system consists of three phases: clustering phase, allocation phase, and path planning phase. It groups all tasks/duties into clusters using the k-means algorithm. After that, it finds the optimal path for each robot to execute its duties in the cluster based on the Nearest neighbor and Harris Hawks Optimizer (HHO). The proposed system is compared with systems that use a genetic algorithm, simulated annealing algorithm, and HHO algorithm. From the finding, we find that the proposed system is more efficient than the other systems in terms of decision time, throughput, and the total distance of each robot.