Control method of rigid-flexible integrated aerial contact operation robot

A control method of a rigid-flexible integrated aerial contact operation robot, wherein the rigid-flexible integrated aerial contact operation robot comprises a fully-actuated multi-rotor unmanned aerial vehicle (UAV) with tilted rotors, a rotation mechanism, and a working arm;

• a central portion of the rotation mechanism is fixed to a central fuselage of the multi-rotor unmanned aerial vehicle, and an outer ring portion of the rotation mechanism is rotationally assembled on the central portion, the working arm is connected to the outer ring portion so as to rotate with the outer ring portion around the central fuselage of the multi-rotor unmanned aerial vehicle, and a plane formed by the working arm during its rotation with the rotation mechanism is perpendicular to a plane collectively defined by shafts of all rotors of the multi-rotor unmanned aerial vehicle, furthermore, during its rotation, the working arm remains clear of and does not interfere with the various rotors and their shafts;
• the working arm comprises a proximal rigid arm connected to the rotation mechanism, a distal rigid arm equipped with an end-effector at its tip, and a soft arm connected between the proximal rigid arm and the distal rigid arm, and the soft arm is a bending soft with a single-section structure;
• comprising following steps:
• step 1:
• constructing a coordinate system group comprising a world coordinate system, a multi-rotor UAV body coordinate system, a rotation mechanism coordinate system, a soft arm base coordinate system, a soft arm end coordinate system, and an end-effector coordinate system, and based on this coordinate system group, establishing a forward kinematics model that describes transformational relationships between motion of the end-effector and motions of other moving parts;
• step 2:
• based on end position of the soft arm, converting it into arc parameters via geometric calculation, and then further converting these into cavity lengths of each bending soft, thereby establishing an inverse kinematics model for the soft arm;
• step 3:
• based on the inverse kinematics model of the soft arm with single-section, using a static pressure-length hysteresis model as the hysteresis model describing hysteretic characteristics of the soft arm to predict the hysteretic characteristics, then based on the hysteresis model, formulating a reinforcement learning-based control algorithm to achieve end-point tracking for the soft arm;
• step 4:
• establishing a dynamic model of a fully-actuated UAV platform with six-degree-of-freedom inputs, subsequently, based on model predictive control (MPC), considering nonlinear system dynamics and external disturbances to implement nonlinear model predictive control (NMPC) using an extended Kalman filter (EKF) estimator to constrain states and inputs, thereby achieving lateral force constraints for the fully-actuated UAV platform with tilted rotors and completing an entire control process.