Abstract: In response to the issues of a wide variety of equipment, large batches, heavy payloads, limited space, complex assembly paths, and high assembly risks inside the cabin, a heavy-duty positioning and assembly robot was designed. Based on the study of the robot kinematics, an error model was established. With the radius of minimum bounding sphere as the constraint condition, the identification results of error parameters by genetic algorithm were compensated into the robot control system. Taking cabinet assembly as an example, a working path was planned based on spatial constraint conditions. By a dynamic constraint energy consumption function model, multi-objective optimal trajectories were obtained with time, impact, and energy consumption as optimization objectives. Prototype testing verified the effectiveness of the error parameter identification, which reduces the absolute positioning errors of the robot. Moreover, the multi-objective optimal trajectory has a small total joint impact and smooth motion, achieving efficient, smooth, and reliable installation of cabinet-type equipment.

Key words: assembly robot, kinematics modeling, multi-objective optimization, error compensation

摘要： 针对舱体类狭长空间内部待安装设备种类多、批量大、载荷重、空间余量微小、装配路径复杂、装配风险高等问题，设计了一种重载调姿装配机器人。在机器人运动学研究的基础上，建立了误差模型，并以最小包围球半径为约束条件，通过遗传算法将误差参数的辨识结果补偿到机器人控制系统。以机柜装配为例，针对空间约束条件规划工作路径，基于动力学约束能耗函数模型，以时间、冲击和能耗为优化目标，得到多目标最优轨迹。样机实验验证了误差参数辨识的有效性，减小了机器人的绝对定位误差，且多目标最优轨迹的关节总冲击小、运动平稳，实现了机柜类设备高效、平稳、可靠的安装。

关键词: 装配机器人, 运动学建模, 多目标优化, 误差补偿