This chapter addresses the challenge of teleoperation for robots designed for highly dynamic tasks, such as homecare or medical assistance, which often include not only robotic arms but also mobile chassis and redundant body motion mechanisms (e.g., humanoid torsos and wheeled chassis). The primary challenge in pose mapping teleoperation is extending control beyond the arm to include the robot’s mobile chassis and other body motions. To meet these needs, the chapter proposes a dynamic gesture trajectory recognition method that captures the operator’s body movements, allowing control of the robot’s mobile chassis and torso motions. The corresponding gesture mapping strategy is designed for the robot’s mobile control to verify the method through functional tests on an integrated nursing assistant robot platform. Additionally, a humanoid dual-arm robot with redundant torso degrees of freedom is used, with the operator’s torso and lower limb movements captured to enable whole-body cooperative teleoperation control. The system is then validated in two key healthcare scenarios: remote homecare (Healthcare 4.0) and telemedicine assistance. This approach demonstrates how the proposed teleoperation system can enhance robot control flexibility and effectiveness in real-world healthcare applications.

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Whole-Body Human-Motion Based Robot Teleoperation

  • Honghao Lyu,
  • Geng Yang,
  • Huayong Yang

摘要

This chapter addresses the challenge of teleoperation for robots designed for highly dynamic tasks, such as homecare or medical assistance, which often include not only robotic arms but also mobile chassis and redundant body motion mechanisms (e.g., humanoid torsos and wheeled chassis). The primary challenge in pose mapping teleoperation is extending control beyond the arm to include the robot’s mobile chassis and other body motions. To meet these needs, the chapter proposes a dynamic gesture trajectory recognition method that captures the operator’s body movements, allowing control of the robot’s mobile chassis and torso motions. The corresponding gesture mapping strategy is designed for the robot’s mobile control to verify the method through functional tests on an integrated nursing assistant robot platform. Additionally, a humanoid dual-arm robot with redundant torso degrees of freedom is used, with the operator’s torso and lower limb movements captured to enable whole-body cooperative teleoperation control. The system is then validated in two key healthcare scenarios: remote homecare (Healthcare 4.0) and telemedicine assistance. This approach demonstrates how the proposed teleoperation system can enhance robot control flexibility and effectiveness in real-world healthcare applications.