Solving Footstep Planning as a Feasibility Problem Using L1-Norm Minimization

Extended version of the paper to be published in IEEE Robotics and Automation LettersInternational audienceOne challenge of legged locomotion on uneven terrains is to deal with both the discrete problem of selecting a contact surface for each footstep and the continuous problem of placing each footstep on the selected surface. Consequently, footstep planning can be addressed with a Mixed Integer Program (MIP), an elegant but computationally-demanding method, which can make it unsuitable for online planning. We reformulate the MIP into a cardinality problem, then approximate it as a computationally efficient l1-norm minimisation, called SL1M. Moreover, we improve the performance and convergence of SL1M by combining it with a sampling-based root trajectory planner to prune irrelevant surface candidates. Our tests on the humanoid Talos in four representative scenarios show that SL1M always converges faster than MIP. For scenarios when the combinatorial complexity is small (< 10 surfaces per step), SL1M converges at least two times faster than MIP with no need for pruning. In more complex cases, SL1M converges up to 100 times faster than MIP with the help of pruning. Moreover, pruning can also improve the MIP computation time. The versatility of the framework is shown with additional tests on the quadruped robot ANYmal

Exploitation of Force Feedback for the Estimation and Control of Walking Robots

Dans cette thèse, on s’intéresse à la commande des robots marcheurs. Contrôler ces systèmes naturellement instables, de dynamique non linéaire, non convexe, de grande dimension, et dépendante des contacts représente un défi majeur en robotique mobile. Les approches classiques formulent une chaîne de contrôle formée d’une cascade de sous problèmes tels que la perception, le planning, la commande du corps complet et l’asservissement articulaire. Les contributions rapportées ici ont toutes pour but d’introduire une rétroaction au niveau de la commande du corps complet ou du planning. Précisément, une première contribution technique est la formulation et la comparaison expérimentale de deux estimateurs de la base du robot. Une seconde contribution est l’implémentation d’un contrôleur par dynamique inverse pour contrôler en couple le robot HRP-2. Une variante de ce contrôleur est aussi formulée et testée en simulation pour stabiliser un robot en contact flexible avec son environnement. Finalement un générateur de marche par commande pré-dictive et couplé à un contrôleur corps complet est présenté.In this thesis, we are interested in the control of walking robots. Controlling these naturally unstable, non-linear, non-convex, large and contact-dependent systems is a major challenge in mobile robotics. Traditional approaches formulate a chain of control formed by a cascade of sub-problems such as perception, planning, full body control and joint servoing. The contributions reported here are all intended to provide state feedback at the whole body control stage or at the planning stage. Specifically, a first technical contribution is the formulation and experimental comparison of two estimators of the robot base. A second contribution is the implementation of a reverse dynamic controller to control the HRP-2 robot in torque. A variant of this controller is also formulated and tested in simulation to stabilize a robot in flexible contact with its environment. Finally, a predictive control operation generator coupled to a whole body controller is presented

Exploitation du Retour de Force pour l'Estimation et le Contrôle des Robots Marcheurs

In this thesis, we are interested in the control of walking robots. Controlling these naturally unstable, non-linear, non-convex, large and contact-dependent systems is a major challenge in mobile robotics. Traditional approaches formulate a chain of control formed by a cascade of sub-problems such as perception, planning, full body control and joint servoing. The contributions reported here are all intended to provide state feedback at the whole body control stage or at the planning stage. Specifically, a first technical contribution is the formulation and experimental comparison of two estimators of the robot base. A second contribution is the implementation of a reverse dynamic controller to control the HRP-2 robot in torque. A variant of this controller is also formulated and tested in simulation to stabilize a robot in flexible contact with its environment. Finally, a predictive control operation generator coupled to a whole body controller is presented.Dans cette thèse, on s’intéresse à la commande des robots marcheurs. Contrôler ces systèmes naturellement instables, de dynamique non linéaire, non convexe, de grande dimension, et dépendante des contacts représente un défi majeur en robotique mobile. Les approches classiques formulent une chaîne de contrôle formée d’une cascade de sous problèmes tels que la perception, le planning, la commande du corps complet et l’asservissement articulaire. Les contributions rapportées ici ont toutes pour but d’introduire une rétroaction au niveau de la commande du corps complet ou du planning. Précisément, une première contribution technique est la formulation et la comparaison expérimentale de deux estimateurs de la base du robot. Une seconde contribution est l’implémentation d’un contrôleur par dynamique inverse pour contrôler en couple le robot HRP-2. Une variante de ce contrôleur est aussi formulée et testée en simulation pour stabiliser un robot en contact flexible avec son environnement. Finalement un générateur de marche par commande pré-dictive et couplé à un contrôleur corps complet est présenté

Simulation aided co-design for robust robot optimization

This paper outlines a bi-level optimization method to concurrently optimize robot hardware parameters and control trajectories that ensure robust performance. The outer loop consists in a genetic algorithm that optimizes the hardware according to its average performance when tracking a locally optimal trajectory in perturbed simulations. The tracking controller exploits the locally optimal feedback gains computed in the inner loop with a Differential Dynamic Programming algorithm, which also finds the optimal reference trajectories. Our simulations feature a complete actuation model, including friction compensation and bandwidth limits. Our method can potentially account for arbitrary perturbations, and it discards hardware designs that cannot robustly track the reference trajectories. Our results show improved performance of the designed platform in realistic application scenarios, autonomously leading to the selection of lightweight and more transparent hardware

Comparative metrics of advanced serial/parallel biped design and characterization of the main contemporary architectures

International audienceThe best achievements in bipedal locomotion have resulted from associating an intelligent and efficient design with clever and robust control. While several control frameworks exist, the design of the legs of our biped robots still lacks a systematic approach and remains a crucial challenge for robot mobility. This paper introduces several criteria to characterize the design of bipedal legs. They aim to guide the design choices and could be implemented in a codesign approach. They reflect the leg overall performances (ability to produce dynamic and accurate foot movements, absorb impacts, lower the motor torques needed to stand up) and characterize the design compactness. We give the algorithmic formulations to evaluate them beyond classical serial designs, to account for any parallel mechanisms. To validate these criteria, we developed a library of open-source CAD models describing the main existing biped architectures, which can be used as a database for future design studies. We discuss the comparative performances of these architectures. We hope this quantified discussion can serve as a baseline to better design future biped robots

Comparison of predictive controllers for locomotion and balance recovery of quadruped robots

International audienceAs locomotion decisions must be taken by considering the future, most existing quadruped controllers are based on a model predictive controller (MPC) with a reduced model of the dynamics to generate the motion, followed by a second whole-body controller to follow the movement. Yet the choice of the considered reduction in the MPC is often ad-hoc or decided by intuition. In this article, we focus on particular MPCs and analyze the effect of the reduced models on the robot behavior. Based on existing formulations, we offer additional controllers to better understand the influence of the reductions in the controller capabilities. Finally, we propose a robust predictive controller capable of optimizing the foot placements, gait period, center-of-mass trajectory and corresponding ground reaction forces. The behavior of these controllers is statistically evaluated in simulation. This empirical study is a basis for understanding the relative importance of the components of the optimal control problem (variables, costs, dynamics), that are sometimes arbitrarily emphasized or neglected. We also provide a qualitative study in simulation and on the real robot Solo