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.

A Hybrid Path Planning and Formation Control Strategy of Multi-Robots in a Dynamic Environment

This paper proposes a hybrid path planning and formation control strategy for multi-robots in a dynamic environment. Under a leader-follower formation structure, the followers can track the motion of one leader after the leader’s path is determined. First, a hybrid path planning strategy that contains global path planning and local path planning of the leader is investigated, in which an improved hybrid grey wolf optimizer with whale optimizer algorithm (GWO-WOA) is designed for the global path planning in a given map, meanwhile, a dynamic window approach (DWA) is fused for the local path planning to avoid dynamic obstacles. Then, a leader-follower formation control algorithm is proposed for multiple mobile robots. The followers are controlled to track their corresponding virtual robots which are generated according to the leader’s position and the formation. Finally, simulation experiments are given to demonstrate the feasibility and effectiveness of the proposed algorithm in different environments.

Model Predictive Control for Cooperative Transportation with Feasibility-Aware Policy

The transportation of large payloads can be made possible with Multi-Robot Systems (MRS) implementing cooperative strategies. In this work, we focus on the coordinated MRS trajectory planning task exploiting a Model Predictive Control (MPC) framework addressing both the acting robots and the transported load. In this context, the main challenge is the possible occurrence of a temporary mismatch among agents’ actions with consequent formation errors that can cause severe damage to the carried load. To mitigate this risk, the coordination scheme may leverage a leader–follower approach, in which a hierarchical strategy is in place to trade-off between the task accomplishment and the dynamics and environment constraints. Nonetheless, particularly in narrow spaces or cluttered environments, the leader’s optimal choice may lead to trajectories that are infeasible for the follower and the load. To this aim, we propose a feasibility-aware leader–follower strategy, where the leader computes a reference trajectory, and the follower accounts for its own and the load constraints; moreover, the follower is able to communicate the trajectory infeasibility to the leader, which reacts by temporarily switching to a conservative policy. The consistent MRS co-design is allowed by the MPC formulation, for both the leader and the follower: here, the prediction capability of MPC is key to guarantee a correct and efficient execution of the leader–follower coordinated action. The approach is formally stated and discussed, and a numerical campaign is conducted to validate and assess the proposed scheme, with respect to different scenarios with growing complexity.