# Design and Simulation of On-Orbit Assembly System Based on Insect-Inspired Transportation
## 4 Dynamic Analysis and Simulation of Patrol Process
In this paper, a cycle of one-leg movement for the robot is defined as follows: first, the thigh moves backward, followed by the folding of the lower leg, causing the foot to leave the ground to facilitate leg movement. The lowest point of the lower leg is maintained at a certain height from the ground. Then, the thigh starts to move forward, and the lower leg starts to extend and prepare to touch the ground. Next, the lowest point of the lower leg touches the ground to become a new contact point for the subsequent cycle. This cycle is repeated to complete the robot's gait. This process can be summarized into the following four stages, as shown in Figure 4a,b:  
1. Backward lifting of the thigh (L4): A gradually reduced clockwise torque is applied to the robot thigh, and the reduced speed (slope) is adjustable. As the torque exceeds the gravity received by the leg, the thigh begins to accelerate clockwise and the lower leg lifts off the ground while moving forward;
2. Forward movement of the lower leg (L5): At the same time, a counterclockwise moment is applied. As the total moment decreases and becomes less than the moment generated by gravity, the leg moves counterclockwise and the lower leg continues to move forward and downward towards the thigh. Thus, the process of this movement can be converted into a change of torque;
3. Forward falling of the thigh (L4): The thigh falls forward to adjust the posture and control the stability;
4. Ground contact of the lower leg (L5): The lower leg relaxes to a certain extent and touches the ground, at which point the robot's supporting leg is switched. As the inertial robot will lean forward, the lower leg touching the ground propels the robot forward for a certain distance. The other leg starts another cycle of one-leg movement, thus repeatedly realizing a cruising movement. To validate the feasibility of the system design, a multibody dynamics simulation of the robot patrol process was conducted. The two-stage patrol process included unloaded and loaded patrols for the in-orbit assembly robot, and reasonable assumptions were made with simplified conditions. When the assembly unit is completed, it is considered a rigid body, while the unit to be assembled and held by the robot end-effector is treated as a flexible body. In order to investigate the influence of the flexible unit on the assembly process, a simulation analysis of the robot gripping and patrolling with the flexible unit was performed. The paper employs the STEP function to control the driving of the robot in ADAMS. By implementing the STEP function, the continuity of velocity and displacement of the robot's joint along the motion path is guaranteed. Maintaining trajectory planning, the study replaces the need for smoothing interpolation of motion parameters by configuring the joint's rotation angles during specific time intervals. The simulation process lasts 18 s, with the robot's legs lifted and lowered a total of eight times during two periods, requiring each joint to rotate at specific angles at different time intervals. During the dynamic simulation of the loaded patrol movement in the robotic assembly, the assembly unit is dozens or even hundreds of times larger than the in-orbit assembly robot, so the assembly unit can cause oscillation during the robot patrol. Therefore, it is necessary to fully consider the flexible characteristics of the unit, and not analyze it as a rigid body. The simulation process is shown in Figure 4b. This study examines a regular hexagonal assembly unit, which has been set as a flexible body and modal analysis carried out to discover the suitable modes for assembly. The unit was modally analyzed in various orders, and its 15-order vibration mode was shown in Figure 4c. Finally, the first six modes of the flexible body were determined and used as input loads in finite element analyses. Compared with rigid-body analysis, flexible-body analysis more accurately predicts the dynamic response of the assembly unit since its load significantly influences its output.