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.
Abstract-Inspired by the command structure of a manned submarine, we have developed a Command and Control (C2) system for autonomous underwater vehicles (AUVs) that allocates mission, navigation and vehicle tasks to individual self-contained agents, each with their own responsibilities and behaviors. These agents are distributed over different levels of control hierarchies where they behave deliberately at the supervisory level and reactively at the vehicle and navigational level. The collective interactions among the pool of agents enables the AUV to achieve its mission objectives autonomously.The mission supervisory level adopts a backseat driver paradigm where mission-level decisions are made based on the inputs provided by a pool of backseat driver (BD) agents. Each BD agent is responsible for handling different aspects of a mission and provides input in the form of mission points to achieve specific mission sub-tasks. This approach offers several advantages. Firstly, complex mission objectives can be divided into simpler mission sub-tasks and handled by different BD agents. Secondly, the C2 system's capabilities in coping with new mission scenarios can be easily extended through the introduction of new BD agents that generates the required maneuvering patterns. New mission behaviors may emerge from the cooperation and/or competition among the BD agents. These complex behaviors increase the level of mission autonomy.The C2 system described above is being used in the STARFISH AUVs and has been used to perform single AUV surveying missions as well as multi-AUV cooperative positioning missions.
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.
This paper focuses on Direct Policy Search (DPS) for cooperative path planning of a single beacon vehicle supporting Autonomous Underwater Vehicles (AUVs) performing surveying missions. Due to lack of availability of GPS signals underwater, the position errors of the AUVs grow with time even though they are equipped with proprioceptive sensors for dead reckoning. One way to minimize this error is to have a moving beacon vehicle with good positioning data transmit its position acoustically from different locations to other AUVs. When the position 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 keep the error bounded. 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. We represent the path planning policy as state-action mapping and employ Variable-Length Genetic Algorithm (VLGA) to automatically discover the number of representative states and their corresponding action mapping. We show the resultant planned paths using the learned policy are able to keep the position errors of the survey AUVs bounded over the mission time.
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