A Benchmarking of DCM Based Architectures for Position and Velocity Controlled Walking of Humanoid Robots

This paper contributes towards the development and comparison of Divergent-Component-of-Motion (DCM) based control architectures for humanoid robot locomotion. More precisely, we present and compare several DCM based implementations of a three layer control architecture. From top to bottom, these three layers are here called: trajectory optimization, simplified model control, and whole-body QP control. All layers use the DCM concept to generate references for the layer below. For the simplified model control layer, we present and compare both instantaneous and Receding Horizon Control controllers. For the whole-body QP control layer, we present and compare controllers for position and velocity control robots. Experimental results are carried out on the one-meter tall iCub humanoid robot. We show which implementation of the above control architecture allows the robot to achieve a walking velocity of 0.41 meters per second.Comment: Submitted to Humanoids201

Whole-Body Trajectory Optimization for Robot Multimodal Locomotion

The general problem of planning feasible trajec-tories for multimodal robots is still an open challenge. This paper presents a whole-body trajectory optimisation approach that addresses this challenge by combining methods and tools developed for aerial and legged robots. First, robot models that enable the presented whole-body trajectory optimisation framework are presented. The key model is the so-called robot centroidal momentum, the dynamics of which is directly related to the models of the robot actuation for aerial and terrestrial locomotion. Then, the paper presents how these models can be employed in an optimal control problem to generate either terrestrial or aerial locomotion trajectories with a unified approach. The optimisation problem considers robot kinematics, momentum, thrust forces and their bounds. The overall approach is validated using the multimodal robot iRonCub, a flying humanoid robot that expresses a degree of terrestrial and aerial locomotion. To solve the associated optimal trajectory generation problem, we employ ADAM, a custom-made open-source library that implements a collection of algorithms for calculating rigid- body dynamics using CasADi

Online Control of Humanoid Robot Locomotion

The complexity of robot dynamics and contact model are only a few of the challenges that increase the online threat to the locomotion problem. During the DARPA Robotics Challenge, a typical strategy to solve the humanoid movement challenge was to construct hierarchical systems made of numerous layers linked in cascade. Each layer computes its output taking into account the information received from the outer layer, the environment, the robot data, and a specific model of the robot and its interaction with the environment. This thesis investigates several model-based controllers for time-critical humanoid robot motion control. Taking into account the layered control architecture, we vary the control models in a crescendo of complexity. Having in mind the importance of designing an online architecture for locomotion, we suggest a framework composed of three layers. The inner layer takes into account the entire robot model, whether kinematic or dynamic. The intermediate and outer layers take into account simpler or reduced models. Given the inner layer, we first develop a controller that takes into account the entire rigid robot dynamical model in the situation of rigid contact with the environment. Second, we remove the rigid contact assumption and design a controller that accounts for compliant walking surfaces. Finally, we eliminate the rigid body hypothesis in some of the robot linkages and propose a controller that takes into account the robot's mechanical flexibility. Considering the outer layers, we first describe a controller that assumes the robot behaves as a simplified model. Then, we seek to eliminate these simplifications while keeping the problem manageable online, by designing a controller that considers only a subset of the robot dynamics. The proposed strategies are tested on real and simulated humanoid robots: the iCub and the TALOS humanoid robots

Capture-Point Based Controllers for Robot Bipedal Locomotion: Analysis and Implementation on the iCub Platform.

Bipedal locomotion is still an open problem in the humanoid community. This thesis proposes a complete controller architecture that allows a humanoid robot to walk on a rigid and flat terrain. First, two controllers are developed to ensure the tracking of the desired divergent component of motion and the zero momentum point. Then, an inverse kinematics algorithm is used to evaluate the desired joint positions. As an additional contribution, a task-based velocity controller has been developed. This can be used, instead of the inverse kinematics algorithm, to reduce the computational effort. Furthermore, since the task-based velocity controller retrieves the robot signal to evaluate the desired joint values it ensures a better tracking of the desired Cartesian tasks, i.e. the desired CoM and feet position and orientation. Experiments in the Gazebo simulator and on the iCub humanoid robot validate the proposed architecture

Dynamic Complementarity Conditions and Whole-Body Trajectory Optimization for Humanoid Robot Locomotion

The paper presents a planner to generate walking trajectories by using the centroidal dynamics and the full kinematics of a humanoid robot. The interaction between the robot and the walking surface is modeled explicitly via new conditions, the \emph{Dynamical Complementarity Constraints}. The approach does not require a predefined contact sequence and generates the footsteps automatically. We characterize the robot control objective via a set of tasks, and we address it by solving an optimal control problem. We show that it is possible to achieve walking motions automatically by specifying a minimal set of references, such as a constant desired center of mass velocity and a reference point on the ground. Furthermore, we analyze how the contact modelling choices affect the computational time. We validate the approach by generating and testing walking trajectories for the humanoid robot iCub.Comment: It is an evolved paper of the conference version available at arXiv:2003.04633. Part of the results have been presented in the first author Ph.D. thesis available at arXiv:2004.0769

Whole-Body Control and Estimation of Humanoid Robots with Link Flexibility

International audienceThis article presents a whole-body controller for humanoid robots affected by concentrated link flexibility. We characterize the link flexibility by introducing passive joints at the concentration of deflections, which separate the flexible links into two or more rigid bodies. In this way, we extend our robot model to take link deflections into account as underactuated extra degrees of freedom, allowing us to design a whole-body controller capable to anticipate deformations. Since in a real scenario, the deflection is not directly measurable, we present an observer aiming at estimating the flexible joint state, namely position, velocity, and torque, only considering the measured contact force and the state of actuated joint. We validate the overall approach in simulations with the humanoid robot TALOS, whose hip is mechanically flexible due to a localized mechanical weakness. Furthermore, the paper compares the proposed wholebody control strategy with state-of-the-art approaches. Finally, we analyze the performance of the estimator in the case of different values of hip elasticity