This paper will present an online approach for friction approximation to be utilized in con- cert with whole body control on humanoid robots. This approach allows humanoid robots with ankle mounted force-torque sensors to extrapolate information about the friction constraints at the hands during multi-contact poses without the addition of hardware to the platform. This is achieved by utilizing disturbance detection as a method of monitoring active forces at a single external point and deriving available friction force at said contact point in accordance with Coulomb's Law of Friction. First, the rigid body dynamics and required compliant humanoid model optimization are established which allow incorporation of friction constraints. These friction constraints are then informed by monitoring of external forces, which can be used as an indicator of slip based on tangential force. In practice, the robot with operational multi-contact whole body control is navigated to the desired contact surface and normal force only contact is initiated. Using an iterative coefficient estimation based on the achieved system forces, the robot tests the boundaries of its operable force range by inducing slip. Slip detection is utilized as the basis for coefficient estimation, which allows the robot to further understand its environment and apply appropriate forces to its contact points. This approach was implemented on a simple 3 link model to verify expected performance, and then on both the simulated model of Virginia Tech's ESCHER robot and in practice on the actual ESCHER platform. The proposed approach was able to achieve estimation of slip parameters, based largely on time spent measuring, actual friction coefficient, and the available contact force. Though the performance of the proposed approach is dependent on a number of variables, it was able to provide an operational parameter for the robot's whole body controller, allowing expansion of the support region without risking multi-contact slip. / Master of Science

With the ability to navigate natural and man-made environments and utilize standard human tools, humanoid robots have the potential to transform emergency response and disaster relief applications by serving as first responders in hazardous scenarios. Such applications will require major advances in humanoid control, enabling robots to traverse difficult, cluttered terrain with both speed and stability. To advance the state of the art, this dissertation presents a complete dynamic locomotion and whole-body control framework for compliant (torque-controlled) humanoids. We develop low-level, mid-level, and high-level controllers to enable low-impedance balancing and walking on compliant and uneven terrain. For low-level control, we present a cascaded joint impedance controller for series elastic humanoids with parallel actuation. A distributed controller architecture is implemented using a dual-axis motor controller that computes desired actuator forces and motor currents using simple models of the joint mechanisms and series elastic actuators. An inner-loop force controller is developed using feedforward and PID control with a model-based disturbance observer, enabling naturally compliant behaviors with low joint impedance. For mid-level control, we implement an optimization-based whole-body control strategy assuming a rigid body model of the robot. Joint torque setpoints are computed using an efficient quadratic program (QP) given desired joint accelerations, spatial accelerations, and momentum rates of change. Constraints on the centroidal dynamics, contact forces, and joint limits ensure admissibility of the optimized setpoints. Using this approach, we develop compliant standing and stepping behaviors based on simple feedback controllers. For high-level control, we present a dynamic planning and control approach for humanoid locomotion using a novel time-varying extension of the Divergent Component of Motion (DCM). By varying the natural frequency of the DCM, we are able to achieve generic vertical center of mass (CoM) trajectories during walking. Complementary reverse-time integration and model predictive control (MPC) strategies are proposed to generate dynamically feasible DCM plans over a multi-step preview window, supporting locomotion on uneven terrain. The proposed approach is validated through experimental results obtained using THOR, a 34 degree of freedom (DOF) series elastic humanoid. Rough terrain locomotion is demonstrated in simulation, and compliant locomotion and push recovery are demonstrated in hardware. We discuss practical considerations that led to a successful implementation on the THOR hardware platform and conclude with an application of the presented control framework for humanoid firefighting onboard the ex-USS Shadwell, a decommissioned Navy ship. / Ph. D.

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.