Philippe Poignet

Optimal Design of a 4-DOF Parallel Manipulator: From Academia to Industry

This paper presents an optimal design of a parallel manipulator aiming to perform pick-and-place operations at high speed and high acceleration. After reviewing existing architectures of high-speed and high-acceleration parallel manipulators, a new design of a 4-DOF parallel manipulator is presented, with an articulated traveling plate, which is free of internal singularities and is able to achieve high performances. The kinematic and simplified, but realistic, dynamic models are derived and validated on a manipulator prototype. Experimental tests show that this design is able to perform beyond the high targets, i.e., it reaches a speed of 5.5 m/s and an acceleration of 165 m/s2. The experimental prototype was further optimized on the basis of kinematic and dynamic criteria. Once the motors, gear ratio, and several link lengths are determined, a modified design of the articulated traveling plate is proposed in order to reach a better dynamic equilibrium among the four legs of the manipulator. The obtained design is the basis of a commercial product offering the shortest cycle times among all robots available in todays market.

Artificial Locomotion Control: from Human to Robots

This paper concerns the simultaneous synthesis and control of walking gaits for biped robots. The goal is to propose an adaptable and reactive control law for two-legged machines. The problem is addressed with human locomotion as a reference. The starting point of our work is an analysis of human walking from descriptive (biomechanics) as well as explicative (neuroscience and physiology) points of view, the objective being to stress the relevant elements for the approach of robot control. The adopted principles are then: no joint trajectory tracking; explicit distinction and integration of postural and walking control; use of evolutive optimization objectives; on-line event handling and environment adaptation and anticipation. This leads to the synthesis of an original control scheme based on non-linear model predictive control: Trajectory Free NMPC. The movement is specified implicitly through coherent physical inequality constraints. Dynamic model and internal limitations of the system are part of the problem constraints. This work is validated by simulation results obtained for the Bip and Rabbit biped robots in various walking and standing situations and compared to human data recorded in these same situations.

Three-dimensional Motion Tracking for Beating Heart Surgery Using a Thin-plate Spline Deformable Model

Minimally invasive cardiac surgery offers important benefits for the patient but it also imposes several challenges for the surgeon. Robotic assistance has been proposed to overcome many of the difficulties inherent to the minimally invasive procedure, but so far no solutions for compensating physiological motion are present in the existing surgical robotic platforms. In beating heart surgery, cardiac and respiratory motions are important sources of disturbance, hindering the surgeon’s gestures and limiting the types of procedures that can be performed in a minimally invasive fashion. In this context, computer vision techniques can be used for retrieving the heart motion for active motion stabilization, which improves the precision and repeatability of the surgical gestures. However, efficient tracking of the heart surface is a challenging problem due to the heart surface characteristics, large deformations and the complex illumination conditions. In this article, we present an efficient method for active cancellation of cardiac motion where we combine an efficient algorithm for 3D tracking of the heart surface based on a thin-plate spline deformable model and an illumination compensation algorithm able to cope with arbitrary illumination changes. The proposed method has two novelties: the thin-plate spline model for representing the heart surface deformations and an efficient parametrization for 3D tracking of the beating heart using stereo images from a calibrated stereo endoscope. The proposed tracking method has been evaluated offline on in vivo images acquired by a DaVinci surgical robotic platform.

Mathematical muscle model for functional electrical stimulation control strategies

In paraplegic patients with upper motor neuron lesions the signal path from the central nervous system to muscles is interrupted. Functional Electrical Stimulation (FES) applied to the lower motor neurons can replace the lacking signals. A neuroprosthesis may be used to restore motor function in paraplegic patients on the basis of FES. The neuroprosthesic implant allows muscles to be controlled with high accuracy, high selectivity and the repeatability of the muscles response can be achieved. The SUAW project succeeded in the implantation of an advanced neuroprosthetic device on two patients, but the movement generation remains open loop and is tuned empirically. The system is thus insufficient to enhance significantly the daily-life of the patient, nevertheless, the good results obtained give us the opportunity to envisage the system evolves towards the automatic synthesis of the stimulation patterns generating the desired movement and closed loop control. To achieve this goal, some preliminary researches have to be carried out; starting with a specific modeling that can be used in the contest of FES. The main issues concern muscle modeling including FES parameters as inputs, fatigue, the interaction with the skeleton, and the identification of parameters. This paper describes the mathematical modeling of the skeletal muscle.

Towards robust 3D visual tracking for motion compensation in beating heart surgery

In the context of minimally invasive cardiac surgery, active vision-based motion compensation schemes have been proposed for mitigating problems related to physiological motion. However, robust and accurate visual tracking remains a difficult task. The purpose of this paper is to present a robust visual tracking method that estimates the 3D temporal and spatial deformation of the heart surface using stereo endoscopic images. The novelty is the combination of a visual tracking method based on a Thin-Plate Spline (TPS) model for representing the heart surface deformations with a temporal heart motion model based on a time-varying dual Fourier series for overcoming tracking disturbances or failures. The considerable improvements in tracking robustness facing specular reflections and occlusions are demonstrated through experiments using images of in vivo porcine and human beating hearts.

Identification of joint stiffness with bandpass filtering

Proposes a method to identify the joint stiffness of a robot using a bandpass filter. It is based on moving one axis at a time. The dynamic model reduces to a model which is linear in relation to a minimum set of dynamical parameters which have to be identified. These parameters are estimated using the least squares solution of an over determined linear system obtained from the sampling of the dynamic model along a closed loop tracking trajectory. Conditions for a good data processing before identification are exhibited through practical aspects concerning data sampling and data filtering. An experimental study shows the efficiency of the method with two sets of data depending on motor joint position measurements.

Beating heart motion prediction for robust visual tracking

In the context of minimally invasive cardiac surgery, robotic assistance has significantly helped surgeons to overcome difficulties related to the minimally invasive procedure. Recently, techniques have been proposed for active canceling the beating heart motion for improving the accuracy of the surgical gestures. In this scenario, computer vision techniques can be applied for estimating the heart motion based solely on natural structures on the heart surface. However, visual tracking is complicated by the particular lighting conditions and clutter (smoke, liquids, etc) during surgery. Another challenging problem are the occasional occlusions by surgical instruments. In order to overcome these problems, we exploit the quasi-periodicity of the beating heart motion for increasing the robustness of the visual tracking task. In this paper, a novel time-varying dual Fourier series for modeling the quasi-periodic beating heart motion is proposed. For estimating the series parameters, an Extended Kalman Filter (EKF) is used. The proposed method is applied in a visual tracking task for bridging tracking disturbances and automatically reestablish tracking in cases of occlusions. The efficiency of the prediction method and the sensible improvements in the visual tracking task are demonstrated through in vivo experiments.

Image Based Visual Servoing through Nonlinear Model Predictive Control

Image based visual servoing (IBVS) is a vision sensor based control architecture. In classical approach, an image Jacobian matrix maps image space errors into errors in Cartesian space. Then a simple proportional control law can be applied guaranteeing local convergence to a desired set point. One of the main advantage of IBVS is its robustness w.r.t camera and robot calibration errors and image measurement errors. Nevertheless, this scheme can not deal with nonlinear constraint such as joint limits and actuator saturation. Visibility constraint is not ensured with classical IBVS. A new IBVS scheme based on nonlinear model predictive control (NMPC) is proposed considering the direct dynamic model of the robot, its joint and torque limits, the camera projection model and the visibility constraint. Simulations exhibit the efficiency and the robustness of the proposed solution to control a 6 degrees of freedom mechanical system

Moving horizon control for biped robots without reference trajectory

This paper deals with a new control approach for biped robots. The technique is inspired by the prediction capability of human being. Optimal computations over a moving horizon are performed with a set of constraints which is modified online to be adapted to the obstacle-filled environment. Simulation results show the efficiency of the algorithm in the case of a gait on flat terrain and steep stairs.

Ultrasound Image-Based Visual Servoing of a Surgical Instrument Through Nonlinear Model Predictive Control

Ultrasound image-guided interventions are widespread in surgery because of the non-invasive character of the procedures. However, hand/eye synchronization is relatively difficult for a surgeon. Ultrasound image-based visual servoing is one way to perform this kind of surgery. In this work, the control of instrument motion based on ultrasound images through nonlinear model predictive control is investigated. This new scheme ensures the convergence of the instrument to the desired position and also offers the possibility of satisfying constraints such as joint limits, actuator saturation and visibility preserving. This paper describes the proposed controller. The efficiency and the robustness of the proposed solution to control a six degree-of-freedom mechanical system is first illustrated by simulation. Experiments on a Mitsubishi PA10 robot highlight the efficiency of the vision control scheme to handle constraints of ultrasound image-based visual servoing.