An autonomous reactive architecture for efficient AUV mission time management in realistic dynamic ocean environment

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• 作者：Somaiyeh Mahmoud Zadeh ; ^{somaiyeh.mahmoudzadeh@flinders.edu.au}Author Vitae ; David M.W. Powers ^{david.powers@flinders.edu.au}Author Vitae ; Karl Sammut ^{karl.sammut@flinders.edu.au}Author Vitae
• 关键词：Autonomous vehicle ; Hierarchical architecture ; Mission/path planning ; Underwater missions ; Task assignment ; Time management ; Decision autonomy
• 刊名：Robotics and Autonomous Systems
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：87
• 期：Complete
• 页码：81-103
• 全文大小：8807 K
• 卷排序：87

文摘

Today AUVs operation still remains restricted to very particular tasks with low real autonomy due to battery restrictions. Efficient path planning and mission scheduling are principle requirement toward advance autonomy and facilitate the vehicle to handle long-range operations. A single vehicle cannot carry out all tasks in a large scale terrain; hence, it needs a certain degree of autonomy in performing robust decision making and awareness of the mission/environment to trade-off between tasks to be completed, managing the available time, and ensuring safe deployment at all stages of the mission. In this respect, this research introduces a modular control architecture including higher/lower level planners, in which the higher level module is responsible for increasing mission productivity by assigning prioritized tasks while guiding the vehicle toward its final destination in a terrain covered by several waypoints; and the lower level is responsible for vehicle’s safe deployment in a smaller scale encountering time-varying ocean current and different uncertain static/moving obstacles similar to actual ocean environment. Synchronization between higher and lower level modules is efficiently configured to manage the mission time and to guarantee on-time termination of the mission. The performance and accuracy of two higher and lower level modules are tested and validated using ant colony and firefly optimization algorithm, respectively. After all, the overall performance of the architecture is investigated in 10 different mission scenarios. The analysis of the captured results from different simulated missions confirms the efficiency and inherent robustness of the introduced architecture in efficient time management, safe deployment, and providing beneficial operation by proper prioritizing the tasks in accordance with mission time.