Visually-guided underwater robots are deployed alongside human divers for cooperative exploration, inspection, and monitoring tasks in numerous shallow-water and coastal-water applications. The most essential capability of such companion robots is to visually interpret their surroundings and assist the divers during various stages of an underwater mission. Despite recent technological advancements, the existing systems and solutions for real-time visual perception are greatly affected by marine artifacts such as poor visibility, lighting variation, and the scarcity of salient features. The difficulties are exacerbated by a host of non-linear image distortions caused by the vulnerabilities of underwater light propagation (e.g., wavelength-dependent attenuation, absorption, and scattering). In this dissertation, we present a set of novel and improved visual perception solutions to address these challenges for effective underwater human-robot cooperation. Specifically, we develop robust and efficient modules for Autonomous Underwater Vehicles (AUVs) to follow and interact with companion divers by accurately perceiving their surroundings while relying on noisy visual sensing alone. Moreover, our proposed perception solutions enable visually-guided robots to see better in noisy sensing conditions and do better with limited computational resources and real-time constraints. The research outcomes entail novel design and efficient implementation of the underlying vision and learning-based algorithms with extensive field experimental validations and feasibility analyses for single-board deployments. In addition to advancing the state-of-the-art, the proposed methodologies and systems take us one step closer toward bridging the gap between theory and practice for improved human-robot cooperation in the wild.
翻译:视觉引导的水下机器人与人类潜水员一起部署,以合作勘探、检查和监测许多浅水和沿海水应用中的合作性勘探、检查和监测任务。这些伴体机器人最基本的能力是直观地解释周围环境,并在水下任务的各个阶段协助潜水员。尽管最近取得了技术进步,但现有系统和实时视觉感知解决方案仍然受到海洋艺术品的极大影响,如可见度低、照明变异和突出特征稀缺等。由于水下光传播的脆弱性(例如,依赖波长的加速、吸收和散布)造成许多非线性图像扭曲,这些困难更加严重。在这种拆解中,我们提出了一套新颖和更好的视觉认知解决方案,以应对水下人类机器人有效合作所面临的这些挑战。具体地说,我们为自动水下潜水器(AUVs)开发了强大和高效的模块,通过精确地透视其周围,同时仅依靠噪音视觉感测,我们提出的视觉感测解决方案使得视觉引导的机器人能够更清楚地看到在遥感条件和精确的理论上的差距,并用有限的逻辑进行更精确的理论分析,从而改进实地的实验性研究结果。