Valerio Ortenzi

An experimental study of robot control during environmental contacts based on projected operational space dynamics

This paper addresses the problem of constrained motion for a manipulator performing a task while in contact with the environment, and proposes a solution based on projected operational space dynamics. The main advantages of this control technique are: 1) it exploits the environment contact constraint itself, so as to minimise the joint torques needed to perform the task; 2) it enables full decoupling of motion and force control; 3) force feedback from a force sensor mounted at the end effector or other contact points is not needed. This work is a step towards a robot control strategy which mimics the human behaviour of exploiting contacts with the environment to help perform tasks. We present an experimental implementation of the control method in which a KUKA LWR IV manipulator uses an eraser to wipe a whiteboard, and we show that this controller can effectively exploit contact with the whiteboard in order to reduce joint torques while still performing the desired wiping motion.

Vision-guided state estimation and control of robotic manipulators which lack proprioceptive sensors

This paper presents a vision-based approach for estimating the configuration of, and providing control signals for, an under-sensored robot manipulator using a single monocular camera. Some remote manipulators, used for decommissioning tasks in the nuclear industry, lack proprioceptive sensors because electronics are vulnerable to radiation. Additionally, even if proprioceptive joint sensors could be retrofitted, such heavy-duty manipulators are often deployed on mobile vehicle platforms, which are significantly and erratically perturbed when powerful hydraulic drilling or cutting tools are deployed at the end-effector. In these scenarios, it would be beneficial to use external sensory information, e.g. vision, for estimating the robot configuration with respect to the scene or task. Conventional visual servoing methods typically rely on joint encoder values for controlling the robot. In contrast, our framework assumes that no joint encoders are available, and estimates the robot configuration by visually tracking several parts of the robot, and then enforcing equality between a set of transformation matrices which relate the frames of the camera, world and tracked robot parts. To accomplish this, we propose two alternative methods based on optimisation. We evaluate the performance of our developed framework by visually tracking the pose of a conventional robot arm, where the joint encoders are used to provide ground-truth for evaluating the precision of the vision system. Additionally, we evaluate the precision with which visual feedback can be used to control the robots end-effector to follow a desired trajectory.

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 robots 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.

Ultrasound imaging for hand prosthesis control: a comparative study of features and classification methods

Controlling a robotic rehabilitation artefact such as a hand prosthesis is yet a rather open problem. Particularly, the choice of a human-machine interface (HMI) to enable natural control is still debatable. The traditional choice, i.e. surface electromyography (sEMG), suffers from a number of problems (electrode displacement, sweat, fatigue) which cannot be easily solved. One of its main drawbacks is the inherent low spatial resolution, at least in the standard settings. To overcome this hindrance, several novel HMIs have been proposed to substitute or augment sEMG; among them, pressure and tactile sensing, and ultrasound imaging (US). In this paper we propose an advancement towards the usage of US as a HMI for hand prosthetics; namely, we compare traditional US image features with Histograms of Oriented Gradients used as input for three classifiers, and show that a high number of hand configurations and grasping force levels can be classified way above chance level by choosing the right combination of features and classifier. In an experiment involving three intact human subjects, a classification accuracy of 80% was obtained; when classifying three different levels of grip force for four grasps, the performance reduces to 60%. These results confirm the usability of US imaging as a HMI for hand prosthetics, and pave the way to its practical usage as a means of natural prosthetic control.

Model estimation and control of compliant contact normal force

This paper proposes a method to realize desired contact normal forces between humanoids and their compliant environment. By using contact models, desired contact forces are converted to desired deformations of compliant surfaces. To achieve desired forces, deformations are controlled by controlling the contact point positions. Parameters of contact models are assumed to be known or estimated using the approach described in this paper. The proposed methods for estimating the contact parameters and controlling the contact normal force are implemented on a LWR KUKA IV arm. To verify both methods, experiments are performed with the KUKA arm while its end-effector is in contact with two different soft objects.

Hybrid motion/force control: a review

Abstract This paper reviews hybrid motion/force control, a control scheme which enables robots to perform tasks involving both motion, in the free space, and interactive force, at the contacts. Motivated by the large amount of literature on this topic, we facilitate comparison and elucidate the key differences among different approaches. An emphasis is placed on the study of the decoupling of motion control and force control. And we conclude that it is indeed possible to achieve a complete decoupling; however, this feature can be relaxed or sacrificed to reduce the robot’s joint torques while still completing the task. Graphical Abstract