Whole body joint load reduction control for high-load tasks of humanoid robot through adapting joint torque limitation based on online joint temperature estimation

In the field of assistive and disaster response robotics, robots must perform long term or momentary high-load tasks, such as holding heavy objects or climbing up and descending from a high step in environments with a lot of disturbances. In such cases, the robot joints could potentially break due to an unintended load during high-load tasks in environments where detection of contact forces is difficult. In this paper, we propose a method for reducing the loads on the joints by limiting the joint torque dynamically based on temperature estimation of these joints. Since joint failure is caused by overheating of a motor, it is important to guarantee that a joint motor temperature remains within the safe limits. Our joint load reduction control is essentially a torque limitation method which bases on adapting the maximum torque given the temperature predictions. Such predictions are extracted from the motor thermal model. To do so, we establish a relationship between the joint temperature and the joint torque. The robot uses such relationship to predict the temperature rise as well as the maximum allowable torque. Next, this maximum torque is feeded to a torque controller in order to achieve load reduction. We experimentally tested our method and confirmed that specific high-load tasks can be achieved even in environments where unintended loads occur.