دانلود کتاب آگاهی از حرکت انسان و ربات از راه دور (درک، ارتباط و کنترل)

Teleoperation is a human-in-the-loop technology for controlling robots remotely. The human operators are responsible for high-level planning and cognitive decisionmaking, while the robot is responsible for manipulation. This allows humans to control the robot remotely without having to be physically present on-site. However, it is difficult for non-expert human operators like healthcare workers to master the control skills of a teleoperated robot in a short time using traditional teleoperation methods. With the development of motion capture and human-robot interaction technologies, human-motion-based teleoperation has become a research focus in the field. However, the mechanisms of human-robot motion mapping strategies are unclear, leading to difficulties in dual-arm motion control and full-body motion control of the robots with high degree-of-actuation. Additionally, the network latency and uncertainty of the communication within the control loop affect the control performance of teleoperation systems. To address these issues, the research of the book works on four aspects: single-arm motion mapping, dual-arm motion mapping, full-body motion mapping, and reliable communication for teleoperation. Moreover, two application cases are conducted to validate the usability of the human motion mapping strategies from the perspective of remote home care and remote medical assistance in Healthcare 4.0. The main research findings are summarized as follows: (1) A human-motion-based mapping framework for single-arm teleoperation is proposed. The framework uses the operator’s upper limb movements as input to control the end effector of the robotic arm. A path-constrained mapping method is proposed to improve the motion trajectory tracking performance of the robot during the teleoperation process. Based on this method, the position tracking deviation between the trajectory of the operator and robot is 1.05 mm at the sampling frequency of 7.5 Hz for simple motion trajectories, while the position tracking deviation is 5.10 mm for complex trajectories. (2) An incremental motion mapping strategy is proposed to eliminate the cumbersome initialization and calibration process typically required in other motion mapping teleoperation systems. It allows flexible adjustments of the operator’s limb posture, improving the convenience and intuitiveness of the teleoperation. Furthermore, a hybrid mapping technique of hand gesture and upper-limb motion (GuLiM) is presented as a novel approach in dual-arm human-robot motion mapping. Comparative experiments showed that the GuLiM method surpassed the Directly Mapping Method (DMM) at the placement precision for position transfer and orientation transfer, with an improvement in accuracy of 46.77% and 69.27%, respectively. (3) A hand gesture trajectory recognition method is proposed using background feature extraction and speed analysis. 10 types of gesture commands are designed for controlling robot locomotion. The recognition algorithm reaches 97.34% recognition accuracy in cross-validation. Further, a trunk motion mapping teleoperation strategy is introduced based on the upper body spine motion and lower limb motion of the operator, achieving intuitive control of the robot trunk with redundant degrees of freedom. Two application cases of elderly care and medical assistance in Healthcare 4.0 are conducted to verify the proposed human-motion-based teleoperation in the indoor environment and hospital isolation ward, respectively. (4) A network hardware-in-the-loop simulation framework is developed to evaluate the impacts of the network performance on remote robot control. The interplay between communication and control is investigated under conditions such as 5G and Wi-Fi 6 local network connections, and a selection rule is given for choosing the wireless networks in local areas with different profiles. Moreover, a cloud-based teleoperation framework is introduced over ultra-long-distance wide-area network connections. A novel feedforward controller is designed to reduce tracking errors between the operator and the robot. This approach has successfully enabled intercontinental human-motion-based teleoperation over 7800 km, from Sweden to China.