[Show abstract][Hide abstract] ABSTRACT: This paper focuses on path planning problem for a single beacon vehicle supporting a team of autonomous underwater vehicles (AUVs) performing surveying missions. Underwater navigation is a challenging problem due to the absence of GPS signal. The positioning error grows with time even though AUVs nowadays are equipped with onboard navigational sensors like compass for dead reckoning. One way to minimize this error is to have a moving beacon vehicle equipped with high accuracy navigational sensors to transmit its position acoustically at strategic locations to other AUVs. When it is received, the AUVs can fuse this data with the range measured from the travel time of acoustic transmission to better estimate their own positions and minimize the error. In this work, we address the beacon vehicle's path planning problem which takes into account the position errors being accumulated by the supported survey AUVs. The resultant path will position the beacon AUV at the strategic locations during the acoustic signal transmission. We formulate the problem within a Markov Decision Process (MDP) framework where the path planning policy is being learned through Cross-Entropy (CE) method. We show that the resultant planned path using the policy learned is able to keep the position error of the survey AUVs bounded throughout the simulated runs.
[Show abstract][Hide abstract] ABSTRACT: Over the past decades, the design and development of mission based Autonomous Underwater Vehicle (AUV) continues to challenge researchers. Although AUV technology has matured and commercial systems have appeared in the market, a generic yet robust AUV command and control (C2) system still remains a key research area. This paper presents a command and control system architecture for modular AUVs. We particularly focus on the design and development of a generic control and software architecture for a single modular AUV while allowing natural extensions to multi-vehicle scenarios. This proposed C2 system has a hybrid modular-hierarchical control architecture. It adopts top-down approach in mission level decision making and task planning while utilizing bottom-up approach for navigational control, obstacle avoidance and vehicle fault detection. Each level consists of one or more autonomous agent components handling different C2 tasks. This structure provides the vehicle developers with an explicit view of the clearly defined control responsibilities at different level of control hierarchy. The resultant C2 system is currently operational on the STARFISH AUV built at the ARL of the National University of Singapore. It has successfully executed some autonomous missions during sea trials carried out around the Singapore coastal area.
Autonomous and Intelligent Systems (AIS), 2010 International Conference on; 07/2010