An innovative decentralized strategy for I-AUVs cooperative manipulation tasks

In the last years, a challenging field of autonomous robotics is represented by cooperative mobile manipulation carried out in different environments (aerial, terrestrial and underwater environment). As regards cooperative manipulation of Intervention-Autonomous Underwater Vehicles (I-AUVs), this application is characterized by a more complex environment of work, compared to the terrestrial or aerial ones, both due to different technological problems, e.g. localization and communication in underwater environment. However, the use of Autonomous Underwater Vehicle (AUV) and I-AUV will necessarily grow up in the future exploration of the sea. Particularly, cooperative I-AUVs represent the natural evolution of single centralized I-AUV because they may be used in various underwater assembly tasks, such as complex underwater structure construction and maintenance (e.g. underwater pipeline and cable transportation can be carried out by multiple cooperative I-AUVs). Furthermore, underwater search and rescue tasks could be more efficient and effective if multiple I-AUVs were used. In this paper, the authors propose an innovative decentralized approach for cooperative mobile manipulation of I-AUVs. This decentralized strategy is based on a different use of potential field method; in particular, a multi-layer control structure is developed to in parallel manage the coordination of the swarm, the guidance and navigation of the I-AUVs and the manipulation task. The main advantage of the potential field method is that less information are necessary: navigation and control problems are reduced to the evaluation of the distance vector among the vehicles, object and obstacles. Moreover, because of the technological problems caused by the underwater environment, the reduction of the transmitted data is one of the keypoints of this architecture. In MATLAB-Simulink, the authors have simulated a transportation task of a partially known object along a reference trajectory in an unknown environment, where some obstacles are placed. The task is performed by a I-AUV swarm composed of four vehicles, each one provided of a 7 Degrees Of Freedom (DOFs) robotic arm.