Planification de mouvement pour robot mobile non-holonome

SIGLEAvailable from INIST (FR), Document Supply Service, under shelf-number : T 78574 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

First Order Approximation of Model Predictive Control Solutions for High Frequency Feedback

International audienceThe lack of computational power on mobile robots is a well-known challenge when it comes to implementing a realtime MPC scheme to perform complex motions. Currently the best solvers are barely able to reach 100Hz for computing the control of a whole-body legged model, while modern robots are expecting new torque references in less than 1ms. This problem is usually tackled by using a handcrafted low-level tracking control whose inputs are the low-frequency trajectory computed by the MPC. We show that a linear state feedback controller naturally arises from the optimal control formulation and can be used directly in the low-level control loop along with other sensitivities of relevant time-varying parameters of the problem. When the optimal control problem is solved by DDP, this linear controller can be computed for cheap as a by-product of the backward pass, and corresponds in part to the classical Riccati gains. A side effect of our proposition is to show that Riccati gains are valuable assets that must be used to achieve an efficient control and that they are not stiffer than the optimal control scheme itself. We propose a complete implementation of this idea on a full-scale humanoid robot and demonstrate its importance with real experiments on the robot Talos

First Order Approximation of Model Predictive Control Solutions for High Frequency Feedback

The lack of computational power on mobile robots is a well-known challenge when it comes to implementing a realtime MPC scheme to perform complex motions. Currently the best solvers are barely able to reach 100Hz for computing the control of a whole-body legged model, while modern robots are expecting new torque references in less than 1ms. This problem is usually tackled by using a handcrafted low-level tracking control whose inputs are the low-frequency trajectory computed by the MPC. We show that a linear state feedback controller naturally arises from the optimal control formulation and can be used directly in the low-level control loop along with other sensitivities of relevant time-varying parameters of the problem. When the optimal control problem is solved by DDP, this linear controller can be computed for cheap as a by-product of the backward pass, and corresponds in part to the classical Riccati gains. A side effect of our proposition is to show that Riccati gains are valuable assets that must be used to achieve an efficient control and that they are not stiffer than the optimal control scheme itself. We propose a complete implementation of this idea on a full-scale humanoid robot and demonstrate its importance with real experiments on the robot Talos

Humanoid human-like reaching control based on movement primitives

This paper deals with the problem of generating realistic human-like reaching movements from a small set of movement primitives. Two kinds of movement databases are used as reference. The first one is obtained numerically, by applying biological principles of motor control on the dynamic model of the robot arm. The second one is obtained by recording reaching movements of human subjects. From these databases, primitives are extracted and analyzed by using Principal Component Analysis. An original generalization method is then proposed for generating movements that did not belong to the initial database. We show that twenty primitives allow to produce new movements, having characteristics similar to that of humans. Experiments on the humanoid robot HRP-2 are presented to illustrate the result

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