ResiVis: A Holistic Underwater Motion Planning Approach for Robust Active Perception Under Uncertainties

摘要

Motion planning for autonomous active perception in cluttered environments remains a challenging problem, requiring real-time solutions that both maximize safety and achieve a desired behavior. In dynamic underwater environments, such as in aquaculture operations, the robots are additionally expected to deal with state and motion uncertainty and errors, dynamic and deformable obstacles, currents, and disturbances. Previous work has introduced real-time frameworks that provided safe navigation in cluttered environments, active perception in static environments, and robust navigation in uncertain dynamic environments. This letter introduces a new real-time approach called ResiVis, which leverages the best aspects of the aforementioned techniques along with a new formulation that further enhances underwater autonomy by enabling active perception of static and dynamic target objects from desired distances. The proposed method utilizes path-optimization for real-time response with constraints guaranteeing continuous collision safety, and computes paths with clearance adaptive to both the conditions of the environments and the performance of the path follower. An improved new constraint encourages observations of dynamic objects with the planner adapting to satisfy desired observation distances and their projected future positions. ResiVis is validated with challenging simulation experiments and with hardware-in-the-loop trials in real industrial-scale aquaculture facilities.