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Optimization of cooperative motion for humanoid robots using a genetic algorithm

机译:利用遗传算法优化人形机器人的合作运动

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Humanoid robots usually consist of a number of joint actuators. Thus, it is essential to adequately control the torques of the joint actuators for achieving desirable motion. Also, torque profile needs to be properly generated especially when a robot is to be moved in cooperation with external environment. For the purpose, this paper proposed a method of optimizing humanoid-robot cooperative motion using a genetic algorithm to find an optimized posture in consideration of torque distribution. An eighteen degree-of-freedom humanoid robot was adopted in this study, and a motion pushing an object was considered. When the robot pushing an object moves from a position to one of the next candidate positions, the torques for all joints were calculated. Then, the torques obtained was reflected to an objective function, which was to be minimized by a genetic algorithm. Here, the objective function was designed in such a way that the surplus torques for all joints should be maximized. As a result of successive minimization processes, a posture optimized for the cooperative motion was found. To show the effectiveness of the proposed method, a series of simulations were carried out for a humanoid robot, and the results were analyzed. The simulation results show that the proposed method can be extended to the control of torques for humanoid robots cooperative with external environment.
机译:人形机器人通常由许多关节执行器组成。因此,必须充分控制关节致动器的扭矩以实现所需的运动。而且,特别是当将机器人与外部环境协作移动时,需要适当地产生扭矩曲线。为目的,本文提出了一种利用遗传算法优化人形机器人协作运动的方法,以考虑扭矩分布来找到优化的姿势。本研究采用了十八自由度的人形机器人,并考虑了推动物体的动作。当推动物体的机器人从位置移动到下一个候选位置之一时,计算用于所有接头的扭矩。然后,所获得的扭矩反映为目标函数,其将通过遗传算法最小化。这里,客观函数的设计使得所有关节的剩余扭矩应该最大化。由于连续最小化过程,找到了对合作运动进行优化的姿势。为了显示所提出的方法的有效性,对人形机器人进行了一系列模拟,并分析了结果。仿真结果表明,该方法可以扩展到人形机器人与外部环境的关系控制。

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