Quadruped robots require compliance to handle unexpected external forces, such as impulsive contact forces from rough terrain, or from physical human-robot interaction. This paper presents a locomotion controller using Cartesian impedance control to coordinate tracking performance and desired compliance, along with Quadratic Programming (QP) to satisfy friction cone constraints, unilateral constraints, and torque limits. First, we resort to projected inverse-dynamics to derive an analytical control law of Cartesian impedance control for constrained and underactuated systems (typically a quadruped robot). Second, we formulate a QP to compute the optimal torques that are as close as possible to the desired values resulting from Cartesian impedance control while satisfying all of the physical constraints. When the desired motion torques lead to violation of physical constraints, the QP will result in a trade-off solution that sacrifices motion performance to ensure physical constraints. The proposed algorithm gives us more insight into the system that benefits from an analytical derivation and more efficient computation compared to hierarchical QP (HQP) controllers that typically require a solution of three QPs or more. Experiments applied on the ANYmal robot with various challenging terrains show the efficiency and performance of our controller.

Legged robots have many potential applications in real-world scenarios where the tasks are too dangerous for humans, and compliance is needed to protect the system against external disturbances and impacts. In this paper, we propose a model-based controller for hierarchical tasks of legged systems subject to external disturbance. The control framework is based on projected inverse dynamics controller, such that the control law is decomposed into two orthogonal subspaces, i.e., the constrained and the unconstrained subspaces. The unconstrained component controls multiple desired tasks with impedance responses. The constrained space controller maintains the contact subject to unknown external disturbances, without the use of any force/torque sensing at the contact points. By explicitly modelling the external force, our controller is robust to external disturbances and errors arising from incorrect dynamic model information. The main contributions of this paper include (1) incorporating an impedance controller to control external disturbances and allow impedance shaping to adjust the behaviour of the motion under external disturbances, (2) optimising contact forces within the constrained subspace that also takes into account the external disturbances without using force/torque sensors at the contact locations. The techniques are evaluated on the ANYmal quadruped platform under a variety of scenarios.

Real world applications require robots to operate in unstructured environments. This kind of scenarios may lead to unexpected environmental contacts or undesired interactions, which may harm people or impair the robot. Adjusting the behavior of the system through impedance control techniques is an effective solution to these problems. However, selecting an adequate impedance is not a straightforward process. Normally, robot users manually tune the controller gains with trial and error methods. This approach is generally slow and requires practice. Moreover, complex tasks may require different impedance during different phases of the task. This paper introduces an optimization algorithm for online planning of the Cartesian robot impedance to adapt to changes in the task, robot configuration, expected disturbances, external environment and desired performance, without employing any direct force measurements. We provide an analytical solution leveraging the mass-spring-damper behavior that is conferred to the robot body by the Cartesian impedance controller. Stability during gains variation is also guaranteed. The effectiveness of the method is experimentally validated on the quadrupedal robot ANYmal. The variable impedance helps the robot to tackle challenging scenarios like walking on rough terrain and colliding with an obstacle.

Hexapod robots have stronger adaptability to dynamic unknown environments than wheeled or trucked ones due to their flexibility. In this paper, a novel control strategy based on rolling gait and trajectory planning, which enables hexapod robots to walk through dynamic environments, is proposed. The core point of this control strategy is to constantly change gait and trajectory according to different environments and tasks as well as stability state of robot. We established a gait library where different kinds of gaits are included. Zero moment point, which indicates the stability of robot, is estimated by a Kalman filter. According to this control strategy, a hierarchical control architecture consisting of a man-machine interface, a vision system, a gait and trajectory planner, a joint motion calculator, a joint servo controller, a compliance controller and a stability observer is presented. The control architecture is applied on a hexapod robot engaging in disaster rescue. Simulation and experimental results show the effectiveness of our control strategy.