International audience

Recently, underactuated bipeds with pointed feet have been studied to achieve dynamic and energy efficient robot walking patterns. However, these studies usually simplify a robot torso as one link, which is different from a human torsos containing 33 vertebrae. In this paper, therefore, we study the optimal walking of a 6-link planar biped with a segmented torso derived from its 5-link counterpart while ensuring that two models are equivalent when the additional torso joint is locked. For the walking, we suppose that each step is composed of a single support phase and an instantaneous double support phase, and two phases are connected by a plastic impact mapping. In addition, the controlled outputs named symmetry outputs capable of generating exponentially stable orbits using hybrid zero dynamics, are adopted to improve physical interpretation. The desired outputs are parameterized by Bezier functions, with 5-link robot having 16 parameters to optimize and 6-link robot having 19 parameters. According to our energy criterion, the segmented torso structure may reduce energy consumption up to 8% in bipedal walking, and the maximum energy saving is achieved at high walking speeds, while leaving the criteria at low walking speeds remain similar for both robots.

International audience

The paper presents a velocity hysteresis friction model. This model can be used for simulation or control of an accurate micropositioning servomechanism. The mechanical and friction parameters are identified from measured signals at low velocities, and with the use of a closed loop identification method. The measured signals are very noised. The number of measurement samples available is very low. The friction model depends on position, velocity and acceleration and includes the Coulomb, Stribeck and viscous frictions. A hysteresis effect integrated by an exponential term and the acceleration is added in the model. The identified parameters are validated by applying an efficient precision method based on the sensitivity analysis of nonlinear models and a normalized mean-square-error criterion.

Friction causes important errors in the control of small servomechanism and should be determined with precision in order to increase the system performance. This paper describes the method to identify the model parameters of a small linear drive with ball-screw. Two kinds of friction models will be applied for the servomechanism looking to rise its micro-positioning abilities. The first one includes the static, viscous and Stribeck friction with hysteresis, and the second one uses the Lugre model. The results will be compared taking into account the criterion error, the accuracy and the normalized mean-square-error of the identified mechanical parameters. The coefficients of the models are identified by a recursive identification method using data acquisition and special filtering technics. The least square identification method is used in this paper in order to establish the motor parameters used as initial condition of the recursive estimation method. Computer simulations and experimental results demonstrate the efficiency of the proposed model.

The helicopter manoeuvres naturally in an environment where the execution of the task can easily be affected by atmospheric turbulences, which lead to variations of its model parameters. The originality of this work relies on the nature of the disturbances acting on the helicopter and the way to compensate them. The disturbance consists in vertical wind gusts. Here, a 7DOF nonlinear Lagrangian model of VARIO helicopter with unknown disturbances is used. This model presents quite interesting control challenges due to nonlinearities, aerodynamical forces, underactuation and its dynamics are non-minmum phase. Two approaches of robust control are compared via simulations: an approximate feedback linearization and an active disturbance rejection control (ADRC) using the approximate feedback linearization procedure. Several simulations show that adding the observer can compensate the effect of disturbances.

The primary goal of this paper is to demonstrate a means to prove asymptotically stable walking in an underactuated, planar, five-link biped robot model. The analysis assumes a rigid contact model when the swing leg impacts the ground and an instantaneous double support phase. The specific robot model analyzed corresponds to a prototype under development by the Centre National de la Recherche Scientifique (CNRS), Paris, France. A secondary goal of the paper is to establish the viability of the theoretically motivated control law. This is explored in a number of ways. First, it is shown how known time trajectories, such as those determined on the basis of walking with minimal energy consumption, can be incorporated into the proposed controller structure. Secondly, various perturbations to the walking motion are introduced to verify disturbance rejection capability. Finally, the controller is demonstrated on a detailed simulator for the prototype which includes torque limits and a compliant model of the walking surface, and thus a noninstantaneous double support phase.

This paper presents the visual servoing of a 6-DOF manipulator for profile following. The profile has an unknown curvature, but its cross-section is known. The visual servoing keeps the transformation constant between a cross-section of the profile and the camera with respect to 6 degrees of freedom. The position of the profile with respect to only five of these degrees of freedom can be measured with the camera, since the image gives no position information along the profile. The kinematic model of the robot is used to reconstruct the displacement along the profile and allow us to control the profile following velocity. Experiments show good accuracy for positioning at a sampling rate of 20 ms. Two control strategies are tested: PI control and generalized predictive control (GPC). The visual servoing exhibits better accuracy with the GPC in simulations and in real experiments.

Presents a way to model visual servoing in the case of a 6 DOF industrial manipulator. The manipulator with its actuators (DC motors), their current feedback loops and their velocity control loops, are modeled as a "virtual Cartesian device". The system with its visual feedback loop is decoupled to form 6 independent loops. Then, 6 generalized predictive controllers (GPC) are implemented online to take into account the dynamics of the manipulator. Simulations and experimental results show a drastic improvement in performance for a 6 DOF industrial manipulator in an eye-in-hand configuration compared to standard approaches neglecting these dynamics.

In this paper, the design of a position tracking control scheme for an industrial robot manipulator is considered. The design is based on the normalized left coprime factors robust stabilization approach. By coupling this H/sub /spl infin// synthesis to a sequential quadratic programming optimization procedure, a systematic design method is developed optimizing the performance of the controller. Further, the role of the nominal model for the design is explored. The control scheme is successfully implemented and tested on a real industrial robot manipulator. The computed controller worked immediately in the first experiment without any further tuning and showed excellent robust performance.

Presents a method for robot modelling called "double dynamic modelling". This method is based on an improvement of the classic dynamic model which takes into account the gear trains included in the robot axes, especially the gear yields and the direction of power transmission in the gears. According to this direction the dynamic model becomes different. Experimental results on a biped robot are given, which show the improved accuracy of the new dynamic model and better performances of the computed-torque control implemented in the biped robot and based on this model.>

In this paper, we present a model-based vision system using a CCD camera mounted on the end-effector of an industrial robot. We describe an autonomous monocular vision system which is used as a feedback signal to control the position and orientation of the manipulator with respect to a dynamic object part of a 3D scene. Without any previous knowledge of the part's placement or motion, this visual feedback would allow a robot to track a pattern in real time. Applications for such a vision-guided robot include 3D object recognition, by extracting 2D image features and rebuilding 3D features. This is performed via rigidity constraint and search comparison in model database geometric inference. Kinematic redundancy is used for general solution of joint velocity represented by the generalized inverse of Jacobian matrix. The problem of redundancy utilization can generally be formulated in the framework of tasks with the order of priority. We present a visual servoing scheme using the task function approach in term of 6 degrees of freedom of robot's gripper regulation. Redundancy theory allows to control position and orientation of the end-effector of an industrial robot in less than 80 ms of cycle time.>