ROApr 12, 2016

A Novel Versatile Architecture for Autonomous Underwater Vehicle's Motion Planning and Task Assignment

arXiv:1604.03308v446 citations
Originality Incremental advance
AI Analysis

This addresses the need for efficient AUV autonomy in underwater missions, though it appears incremental as it builds on existing planning frameworks.

The paper tackles the problem of robust decision-making for Autonomous Underwater Vehicles (AUVs) in unknown environments by developing a conflict-free task assignment architecture with global and local planners, resulting in enhanced mission productivity, time management, and safety as demonstrated in simulations.

Expansion of today's underwater scenarios and missions necessitates the requestion for robust decision making of the Autonomous Underwater Vehicle (AUV); hence, design an efficient decision making framework is essential for maximizing the mission productivity in a restricted time. This paper focuses on developing a deliberative conflict-free-task assignment architecture encompassing a Global Route Planner (GRP) and a Local Path Planner (LPP) to provide consistent motion planning encountering both environmental dynamic changes and a priori knowledge of the terrain, so that the AUV is reactively guided to the target of interest in the context of an unknown underwater environment. The architecture involves three main modules: The GRP module at the top level deals with the task priority assignment, mission time management, and determination of a feasible route between start and destination point in a large scale environment. The LPP module at the lower level deals with safety considerations and generates collision-free optimal trajectory between each specific pair of waypoints listed in obtained global route. Re-planning module tends to promote robustness and reactive ability of the AUV with respect to the environmental changes. The experimental results for different simulated missions, demonstrate the inherent robustness and drastic efficiency of the proposed scheme in enhancement of the vehicles autonomy in terms of mission productivity, mission time management, and vehicle safety.

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