# Research on Human-Robot Collaboration Method for Parallel Robots Oriented to Segment Docking
## 1 Introduction  
Assembling large-scale aerospace equipment involves cabin segment docking, a pivotal aspect in manufacturing that directly impacts product precision and stability [1]. Conventional manual docking methods, requiring high precision in terms of worker operation and involving labor-intensive processes, fall short of meeting practical application demands [2]. Parallel robots boast advantages such as a high load capacity, precise positional control, and low end-effector inertia, making them ideal for assembling large or heavy components [3]. Therefore, employing parallel robots for cabin segment docking and developing a high-precision, high-efficiency docking system is of significant importance. Aerospace cabin segments, which are usually large and heavy, require precise docking without collisions or pressure to safeguard against damage to the segments and internal seals during docking procedures [4]. Achieving fully automated, high-precision and high-reliability docking is challenging as it often depends on manual adjustments during thecabin segment docking process to ensure precision and reliability [5]. Maximizing the strengths of robots and humans, merging human expertise with parallel robots' precise positioning, and employing collaborative human-robot methods through parallel robots for segment docking offer an effective and practical path to accomplish efficient, precise, and reliable docking procedures. Space cabin segments require docking precision in the tens of micrometers while their substantial mass causes significant inertia during movement. Precise adjustments and significant inertial effects often lead to collisions during docking, posing a greater risk of segment damage [6]. Resolving this problem may require implementing a high-compliance strategy for Human-robot collaboration. This approach entails real-time adjustments to the robot's movements, compensating for the extra motion induced by inertia in the docking process, effectively averting unforeseen movements that could result in collisions. The present research mainly concentrates on perceiving operator intentions using diverse methods to attain compliance in human-robot collaboration (HRC) [7]. [PERSON] et al. introduced a structured approach concentrating on user experience (UX), devising multiple interaction design tools to scrutinize user experience and human-robot dialogues [8]. [PERSON] et al. equipped the end effector of a three-degree-of-freedom parallel robot with a pen, enabling operators to collaboratively guide the robot through maze-like paths [9]. In their prior research, they finetuned the robot's control rules by interpreting human intentions [10]. [PERSON] et al. employed a GA-BP neural network to independently learn the correlation between input parameters and output velocities, maintaining this unknown correlation within the network structure. This allowed for the continuous prediction of operator intention information to derive the operator's intended actions [11]. [PERSON] et al. suggested a hybrid recursive neural network structure utilizing deep learning to allow for robots to discern human intentions in collaborative tasks [12]. The present research solely categorizes and recognizes operator intentions by interaction force, adjusting robot control parameters accordingly. However, these studies lack an exact, quantifiable link between adjustment strategies and interaction forces. Hence, it is crucial to further explore the correlation between interaction forces and adjustment strategies to achieve precise human-parallel robot collaboration for cabin segment docking. Adapting the robot's movements to prevent segment collisions during docking necessitates rapid responses to interaction forces in collaboration. However, the closed-loop structure of parallel robots induces coupled disturbance torques arising from interconnected inertia among their drive axes. This leads to control overshoot and vibrations during motion control, significantly affecting the robot's dynamic response [13]. [PERSON] et al.