Robust Whole-Body Motion Control of Legged Robots

We introduce a robust control architecture for the whole-body motion control of torque controlled robots with arms and legs. The method is based on the robust control of contact forces in order to track a planned Center of Mass trajectory. Its appeal lies in the ability to guarantee robust stability and performance despite rigid body model mismatch, actuator dynamics, delays, contact surface stiffness, and unobserved ground profiles. Furthermore, we introduce a task space decomposition approach which removes the coupling effects between contact force controller and the other non-contact controllers. Finally, we verify our control performance on a quadruped robot and compare its performance to a standard inverse dynamics approach on hardware.Comment: 8 Page

Practical Model-based and Robust Control of Parallel Manipulators Using Passivity and Sliding Mode Theory

This chapter provides a practical strategy to realize accurate and robust control for 6 DOFs (degrees of freedom) parallel robots. The presented approach consists in two parts. The first basic part is based on the the compensation of the desired dynamics in combination with controller/observer for the single actuators. The passivity formalism offers an excellent framework to design and to tune the closed-loop dynamics, such that the desired behavior is obtained. The basic algorithm is proved to be locally robust towards uncertainties. The second part of the control strategy consists in a sliding mode controller. To keep the practical and computational efficient implementation, the proposed switching control considers explicitly only the friction model. Here we opt for the so called model-decomposition paradigm and we use additional integral action to improve robustness. The proposed approach is substantiated with experimental results demonstrating the effectiveness and success of the strategy that keeps control setup simple and intuitive. Keywords parallel manipulators, robust control, passivity formalism, sliding mode control, desired dynamics compensation, velocity observe

Fast Manipulability Maximization Using Continuous-Time Trajectory Optimization

A significant challenge in manipulation motion planning is to ensure agility in the face of unpredictable changes during task execution. This requires the identification and possible modification of suitable joint-space trajectories, since the joint velocities required to achieve a specific endeffector motion vary with manipulator configuration. For a given manipulator configuration, the joint space-to-task space velocity mapping is characterized by a quantity known as the manipulability index. In contrast to previous control-based approaches, we examine the maximization of manipulability during planning as a way of achieving adaptable and safe joint space-to-task space motion mappings in various scenarios. By representing the manipulator trajectory as a continuous-time Gaussian process (GP), we are able to leverage recent advances in trajectory optimization to maximize the manipulability index during trajectory generation. Moreover, the sparsity of our chosen representation reduces the typically large computational cost associated with maximizing manipulability when additional constraints exist. Results from simulation studies and experiments with a real manipulator demonstrate increases in manipulability, while maintaining smooth trajectories with more dexterous (and therefore more agile) arm configurations.Comment: In Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS'19), Macau, China, Nov. 4-8, 201