Identifying External Contacts from Joint Torque Measurements on Serial Robotic Arms and Its Limitations

Abstract

The ability to detect and estimate external contacts is essential for robot arms to operate in unstructured environments occupied by humans. However, most robot arms are not equipped with adequate sensors to detect contacts on their entire body. What many robot arms do have is torque sensors for individual joints. Through a quantitative analysis, we argue that it is fairly likely for two distinct contacts on the robot’s surface to generate almost identical joint torque measurements. When this happens, the best contact estimate achievable is the set of possible contact positions, all of which would reproduce the measured joint torque. Searching for elements of this set is equivalent to solving to global optimality a nonlinear program.By combining rejection sampling with gradient descent, we propose a contact estimation method which in practice finds all local optima of the nonlinear program at real-time rates. In addition, we propose an active contact exploration method which falsifies spurious contact estimates in the set of local optima by making small motions around the robot’s current configuration. The proposed methods highlight the caveats of contact estimation from only joint torque, which, coupled with known limitations of such estimators, suggest that a more capable sensor is probably needed for robust whole-body contact estimation.