Optimization of Potential Field Method Parameters through networks for Swarm Cooperative Manipulation Tasks

An interesting current research field related to autonomous robots is for sure the mobile manipulation performed by cooperating robots (in terrestrial, aerial and underwater environments). Focusing on the underwater scenario, cooperative manipulation of Intervention-Autonomous Underwater Vehicles (I-AUVs) is a complex and not easy application with respect to the terrestrial or aerial ones because of many technical issues, such as underwater localization and limited communication. A decentralized approach for cooperative mobile manipulation of I-AUVs based on Artificial Neural Networks (ANNs) is proposed in this paper. This strategy exploits the potential field method; a multi-layer control structure is developed to manage the coordination of the swarm, the guidance and navigation of I-AUVs and the manipulation task. In the paper, this new strategy has been implemented in the simulation environment, simulating the transportation of an object. This object is moved along a desired trajectory in an unknown environment and it is transported by four underwater mobile robots, each one provided of a 7 Degree Of Freedoms (DOFs) robotic arm. The simulation results are optimized thanks to the Artificial Neural Networks (ANNs) used for the potentials tuning.