Research On Visual Detection And Tracking For Underwater Robot Picking

For the past few years,the research of autonomous grasping by underwater robots enjoys wide popularity with the development of operational underwater robot technology,the usage of high-quality underwater cameras,and the increasing demand for seafood fishing.Underwater optical vision detection and tracking is the core technology of autonomous underwater grasping operations.What is particular,the accuracy of detection and tracking is crucial to complete the autonomous grasping task.Since the difficulty of optical vision detection and tracking of marine benthic organisms is how to provide visual guidance continuously and accurately for the robot control system in the process of autonomous robot grasping,especially for the more complex marine environment.According to the characteristics of the benthic organisms,water environment,and robot operation,this thesis focuses on developing a more efficient,accurate real-time visual detection and tracking system and applying the visual system to an underwater robot.The main research contents are summarized as follows:First of all,a multi-level customized benthic visual detection algorithm is proposed from the perspective of the characteristics of marine benthic organisms.The multi-level optimization design of the baseline network of benthic marine organisms target detection is constructed by considering the requirements of autonomous grasping tasks of underwater robots and combined with the baseline method.To a certain extent,this algorithm alleviates the detection difficulties caused by factors such as the deformation of the appearance of sea creatures,the special viewing angle of the robot,and the uneven growth and distribution of benthic creatures.Secondly,from the perspective of the characteristics of the water environment,and considering the impact of the visibility of the marine environment on the detection performance,a two-stage visual detection algorithm that can effectively alleviate the decline in detection accuracy caused by turbid water is proposed.Key content attention items,query and key content attention items,key content,and relative position attention item are introduced into the backbone,RPN,and FPN respectively.We reckon the advantage of different Hierarchical attention information can alleviate the problem of blurred goals caused by turbid water.Thus,this algorithm is effective in detecting benthic organisms in turbid water.Finally,owing to the in-depth analysis of coring-related filtering theory,we analyze the reasons for the poor performance when directly applying KCF to underwater autonomous grasping tasks.Therefore,a self-discrimination module based on uncertainty is proposed,which includes the uncertainty self-discrimination mechanism and three auxiliary discrimination strategies.The improved KCF tracker can handle special challenges while grasping.