Kinematics-Based Estimation of Contact Constraints Using Only Proprioception

Robots are increasingly being required to perform tasks which involve contacts with the environment. This paper addresses the problem of estimating environmental constraints on the robot's motion. We present a method which estimates such constraints, by computing the null space of a set of velocity vectors which differ from commanded velocities during contacts. We further extend this method to handle unilateral constraints, for example when the robot touches a rigid surface. Unlike previous work, our method is based on kinematics analysis, using only proprioceptive joint encoders, thus there is no need for either expensive force-torque sensors or tactile sensors at the contact points or any use of vision. We first show results of experiments with a simulated robot in a variety of situations, and we analyse the effect of various levels of observation noise on the resulting contact estimates. Finally we evaluate the performance of our method on two sets of experiments using a KUKA LWR IV manipulator, tasked with exploring and estimating the constraints caused by a horizontal surface and an inclined surface

Unsupervised Contact Learning for Humanoid Estimation and Control

This work presents a method for contact state estimation using fuzzy clustering to learn contact probability for full, six-dimensional humanoid contacts. The data required for training is solely from proprioceptive sensors - endeffector contact wrench sensors and inertial measurement units (IMUs) - and the method is completely unsupervised. The resulting cluster means are used to efficiently compute the probability of contact in each of the six endeffector degrees of freedom (DoFs) independently. This clustering-based contact probability estimator is validated in a kinematics-based base state estimator in a simulation environment with realistic added sensor noise for locomotion over rough, low-friction terrain on which the robot is subject to foot slip and rotation. The proposed base state estimator which utilizes these six DoF contact probability estimates is shown to perform considerably better than that which determines kinematic contact constraints purely based on measured normal force.Comment: Submitted to the IEEE International Conference on Robotics and Automation (ICRA) 201

Unsupervised Contact Learning for Humanoid Estimation and Control

This work presents a method for contact state estimation using fuzzy clustering to learn contact probability for full, six-dimensional humanoid contacts. The data required for training is solely from proprioceptive sensors - endeffector contact wrench sensors and inertial measurement units (IMUs) - and the method is completely unsupervised. The resulting cluster means are used to efficiently compute the probability of contact in each of the six endeffector degrees of freedom (DoFs) independently. This clustering-based contact probability estimator is validated in a kinematics-based base state estimator in a simulation environment with realistic added sensor noise for locomotion over rough, low-friction terrain on which the robot is subject to foot slip and rotation. The proposed base state estimator which utilizes these six DoF contact probability estimates is shown to perform considerably better than that which determines kinematic contact constraints purely based on measured normal force.Comment: Submitted to the IEEE International Conference on Robotics and Automation (ICRA) 201

Vision-based trajectory control of unsensored robots to increase functionality, without robot hardware modication

In nuclear decommissioning operations, very rugged remote manipulators are used, which lack proprioceptive joint angle sensors. Hence these machines are simply tele-operated, where a human operator controls each joint of the robot individually using a teach pendant or a set of switches. Moreover, decommissioning tasks often involve forceful interactions between the environment and powerful tools at the robot's end-effector. Such interactions can result in complex dynamics, large torques at the robot's joints, and can also lead to erratic movements of a mobile manipulator's base frame with respect to the task space. This Thesis seeks to address these problems by, firstly, showing how the configuration of such robots can be tracked in real-time by a vision system and fed back into a trajectory control scheme. Secondly, the Thesis investigates the dynamics of robot-environment contacts, and proposes several control schemes for detecting, coping with, and also exploiting such contacts. Several contributions are advanced in this Thesis. Specifically a control framework is presented which exploits the constraints arising at contact points to effectively reduce commanded torques to perform tasks; methods are advanced to estimate the constraints arising from contacts in a number of situations, using only kinematic quantities; a framework is proposed to estimate the configuration of a manipulator using a single monocular camera; and finally, a general control framework is described which uses all of the above contributions to servo a manipulator. The results of a number of experiments are presented which demonstrate the feasibility of the proposed methods