The identification of human joint impedance is necessary for various applications, such as improving rehabilitation efficiency or monitoring the human operator's state (fatigue, stress). To this end, in this paper we combine robot's payload identification methods with sliding window recursive least squares algorithm allowing a continuous identification of the varying human joint model without the need for external sensors. We also propose a threshold for detecting fake changes in the identified model parameters due to numerical issues. The presented approach is validated by simulations and experiments using elastic rubber bands representing a simplified passive human joint model attached to a one degree of freedom robotic system. Comparison with simple recursive least squares shows that the proposed method is promising, as it converges to the new parameter in a single window length, whereas the other method takes much longer. In addition, it distinguishes real from fake changes depending on the validity of the used model.

Traditional identification approaches for robotic systems based on the inverse dynamic model and the least-squares method are the most used to identify dynamic parameters of robots. However these methods often require a well-tuned filtering or estimation of the position, velocity, acceleration and torque to avoid bias in identification results. The cutoff frequency of the low-pass filter that is usually used must be well chosen, which is not always a trivial task. In this paper, we propose to use an extended Kalman filter to reduce the noise on the measured position and to estimate the velocity and acceleration. These estimates can then be fed to the controller to further reduce the noise in the control torque. The effect of the tuning of this filter is examined and the presented approach is validated through simulations and experiments on a one degree of freedom system.

International audience

International audience

Needle insertion procedures can greatly benefit from robotic systems to improve their accuracy and success rate. However, a fully automated system is usually not desirable and the clinicians need to be included in the control loop. In this paper we present a teleoperation framework for beveledtip flexible needle steering that enables the user to directly and intuitively control the trajectory of the needle tip via a haptic interface. The 6 degrees of freedom of the needle base are used to perform several automatic safety and targeting tasks in addition to the one controlled by the user. Real-time visual feedback is provided by a 3D ultrasound probe and used to track the 3D location of the needle and of a spherical target. Several haptic force feedback are compared as well as two different levels of mix between automated and user-controlled tasks. A validation of the framework is conducted in gelatin phantom and a mean targeting accuracy of 2.5 mm is achieved. The results show that providing an adequate haptic guidance to the user can reduce the risks of damage to the tissues while still letting the surgeon in control of the tip trajectory.

Works on robotic needle steering often consider either tip-based control of flexible beveled tip needles or control of the bending of symmetric tip needles. In this paper a control law for needle steering which uses both direct manipulation of the needle base and the control of the tip deflection, is proposed. A 3D model of a beveled tip needle is used to determine the base motion that is needed to obtain the desired tip motion. The needle-tissue interaction model uses local virtual springs placed along the needle shaft and takes into account the interaction forces at the bevel. Online estimation and update of this model parameters is performed via visual feedback. The low level controller uses the task function framework to allow control of the tip velocity. Additionally duty cycling method is used if a reduction of the natural deflection of the needle tip is needed. Finally an experimental targeting task in a gelatin phantom is presented for preliminary validation of the framework. The method provided sub-millimeter accuracy on a target that would be unreachable using only tip-based control.

With the increasing number of clinical interventions using needle shaped tools, robotic control of needle insertion procedures has been an active research field for many years. In this work we propose a 3D model of a flexible needle that takes into account tissue deformations in order to predict the needle shape and trajectory when it is inserted using a robotic arm. To account for tissue displacements, we designed a method based on visual feedback that updates the interaction model between the needle and the tissue using an unscented Kalman filter. Results obtained from several needle insertions in a soft tissue phantom showed that the method gives good performance in terms of needle trajectory prediction. This model was also considered in a closed-loop control approach to allow automatic reaching of a target.