Abstract: A multi-robot collaborative planning method for machining a large capsule member of a Spacecraft is described. The method comprises: first, planning the number of instances of rotational displacement of a capsule, and the angle of each rotation; then, planning a multi-robot station layout and station switching strategy; and finally, when the position of the capsule and robot stations are fixed, planning a multi-robot machining task time sequence. The machining process for a large capsule is efficiently planned by selecting optimal rotation schemes and robot station positions, enhancing the rigidity of robot collaboration. This planning also streamlines the machining timeline, making the multi-robot task more compact and reducing idle time, thus boosting overall machining efficiency.

Abstract: A multi-robot collaborative planning method for machining a large capsule member of a Spacecraft is described. The method comprises: first, planning the number of instances of rotational displacement of a capsule, and the angle of each rotation; then, planning a multi-robot station layout and station switching strategy; and finally, when the position of the capsule and robot stations are fixed, planning a multi-robot machining task time sequence. The machining process for a large capsule is efficiently planned by selecting optimal rotation schemes and robot station positions, enhancing the rigidity of robot collaboration. This planning also streamlines the machining timeline, making the multi-robot task more compact and reducing idle time, thus boosting overall machining efficiency.

Abstract: Disclosed is a variable-parameter stiffness identification and modeling method for an industrial robot. An effective working space of a robot is divided into a plurality of cubic regions. For an operating task in a certain machining region, different loads are applied to an end effector at multiple positions and multiple postures in the region, and robot joint stiffness in this section is identified and acquired according to the relationship between the loads and an end deformation, thereby realizing accurate stiffness control of the robot in different operating sections during a machining process.

Abstract: A robot vision-based automatic rivet placement system and method. The automatic rivet placement system includes: an industrial robot installed on a frame, a multi-functional end effector, a rivet blowing mechanism, a detection disk, and a rivet holding tray. The multi-functional end effector consists of a flange disk, a support frame, an industrial CCD camera, a laser displacement sensor, a spring, a mixing rod, and a vacuum nozzle. The multi-functional end effector is connected to a terminal end of the industrial robot via the flange disk. The industrial CCD camera is installed directly in front of the support frame, and is used to acquire a rivet image and measure a rivet parameter. The laser displacement sensor is installed at a side surface of the support frame, and is used to measure a rivet depth.

Abstract: Disclosed is a variable-parameter stiffness identification and modeling method for an industrial robot. An effective working space of a robot is divided into a plurality of cubic regions. For an operating task in a certain machining region, different loads are applied to an end effector at multiple positions and multiple postures in the region, and robot joint stiffness in this section is identified and acquired according to the relationship between the loads and an end deformation, thereby realizing accurate stiffness control of the robot in different operating sections during a machining process.

Abstract: A robot vision-based automatic rivet placement system and method. The automatic rivet placement system includes: an industrial robot installed on a frame, a multi-functional end effector, a rivet blowing mechanism, a detection disk, and a rivet holding tray. The multi-functional end effector consists of a flange disk, a support frame, an industrial CCD camera, a laser displacement sensor, a spring, a mixing rod, and a vacuum nozzle. The multi-functional end effector is connected to a terminal end of the industrial robot via the flange disk. The industrial CCD camera is installed directly in front of the support frame, and is used to acquire a rivet image and measure a rivet parameter. The laser displacement sensor is installed at a side surface of the support frame, and is used to measure a rivet depth.