Research On Local Autonomy Based On Vision In Space Robot Teleoperation

Utilizing space robot to realize on-orbit servicing of spacecraft represents space servicing technical trend. However, it is difficult to develop an autonomous intelligent space robot in a short time due to the limit of current sensor comprehension, control of robot motion and artificial intelligence. As a result, current on-orbit space robot is mainly controlled in teleoperation method. However, long time delay and unadaptive to environment is a general obstacle in traditional teleloperation, which directly affects performance of space telerobotics system. Therefore, it is significant for space robot to study integration of vision technology and teleoperation, which not only overcome teleoperation time delay, but also improve autonomy performance and enlarge application. This dissertation focus on local autonomy based on vision in space robot teleoperation under surporting of the project"study on the key technologies of satellite on-orbit self-servicing and its teleoperation".This dissertation can be mainly classified into two parts for disposing of time delay in space robot teleoperation system. On one hand, from the view of improving traditonal teleoperation performance, a vision aid teleoperation is investigtaed by combining virtual reality and real vision data. On the other hand, object recognition and visual servoing in local autonomy of space robot is detailly investigated in order to completely cut off time delay in teleoperation system.Satellite self-serving teleoperation system is established in this dissertation. The three-dimensional graphic predictive display is realized by virtual reality to overcome the influence of time delay on teleoperation. In order to deal with mismatch of bandwidth and video stream bit rate, a bandwidth adaptive scheme based on scalable bit rate control is proposed to realize video feedback of remote robot part. Accordingly, visual telepresence experience of teleoperation system is highly improved.To accomplish local autonomy of space robot in structured environment and unstructured environment, this dissertation focuses on object recognition and visual servoing of local autonomy. For the structured environment, a model-based object recognition is investigated based on vector compression and shape approximation. Furthermore, sub-pixel of corner feature is extracted and optimized to realize precise and stable recognition of model-based object. Utilizing vision data of model-object, object pose is estimated based on non-linear optimization. According to on-line estimation of depth and rotation pose of object, a hybrid visual servoing method is proposed to guarantee precision and range in local automation of space robot. Based on characteristic of self motion in redundant freedom robot, a partitioned visual servoing is realized to handle camera retreat in image-based visual servoing.For the unstructured environment, in order to improve the adaptability of vision system for various environments, an object recognition strategy based on multi distributing visual feature integration is proposed, which implement robust recognition and tracking of feature in complex and dynamic environment. To realize space robot teleoperation under unstructured environment, a teleoperation strategy is presented based on combination task-level observer built by dynamic visual inspection, operator grasping experience and local autonomy of remote robot, which improve the systeme operation performance and capability of handling accident.At last, theory and method proposed in the dissertation are tested by experiment based on the ground teleoperation demonstration system of satellite self-serving. In the structured environment, a probe insert experiment under wide range is carried out with hybrid visual servoling, which enlarge automation operation space of satellite self-serving system effectively. In the unstructured environment, utilizing task-level regulation, the experiment of rubbing camera lens are realized based on dynamic visual inspection of operation scene.