We present a computationally efficient method for online planning of bipedal walking trajectories with push recovery. In particular, the proposed methodology fits control architectures where the Divergent-Component-of-Motion (DCM) is planned beforehand, and adds a step adapter to adjust the planned trajectories and achieve push recovery. Assuming that the robot is in a single support state, the step adapter generates new positions and timings for the next step. The step adapter is active in single support phases only, but the proposed torque-control architecture considers double support phases too. The key idea for the design of the step adapter is to impose both initial and final DCM step values using an exponential interpolation of the time varying ZMP trajectory.This allows us to cast the push recovery problem as a Quadratic Programming (QP) one, and to solve it online with state-of-the-art optimisers. The overall approach is validated with simulations of the torque-controlled 33 kg humanoid robot iCub. Results show that the proposed strategy prevents the humanoid robot from falling while walking at 0.28 m/s and pushed with external forces up to 150 Newton for 0.05 seconds.
IEEE-RAS International Conference on Humanoid Robots (HUMANOIDS 2019)

In this paper, previous works on the Model Predictive Control (MPC) and the Divergent Component of Motion (DCM) for bipedal walking control are extended. To this end, we employ a single MPC which uses a combination of Center of Pressure (CoP) manipulation, step adjustment, and Centroidal Moment Pivot (CMP) modulation to design a robust walking controller. Furthermore, we exploit the concept of time-varying DCM to generalize our walking controller for walking in uneven surfaces. Using our scheme, a general and robust walking controller is designed which can be implemented on robots with different control authorities, for walking on various environments, e.g. uneven terrains or surfaces with a very limited feasible area for stepping. The effectiveness of the proposed approach is verified through simulations on different scenarios and comparison to the state of the art.
IEEE International Conference on Robotics and Automation (ICRA)

In this paper, a combination of ankle and hip strategy is used for push recovery of a position-controlled humanoid robot. Ankle strategy and hip strategy are equivalent to Center of Pressure (CoP) and Centroidal Moment Pivot (CMP) regulation respectively. For controlling the CMP and CoP we need a torque-controlled robot, however most of the conventional humanoid robots are position controlled. In this regard, we present an efficient way for implementation of the hip and ankle strategies on a position controlled humanoid robot. We employ a feedback controller to compensate the capture point error. Using our scheme, a simple and practical push recovery controller is designed which can be implemented on the most of the conventional humanoid robots without the need for torque sensors. The effectiveness of the proposed approach is verified through push recovery experiments on SURENA-Mini humanoid robot under severe pushes.
IEEE/RSI International Conference on Robotics and Mechatronics (ICROM)

The three bio-inspired strategies that have been used for balance recovery of biped robots are the ankle, hip and stepping Strategies. However, there are several cases for a biped robot where stepping is not possible, e. g. when the available contact surfaces are limited. In this situation, the balance recovery by modulating the angular momentum of the upper body (Hip-strategy) or the Zero Moment Point (ZMP) (Ankle strategy) is essential. In this paper, a single Model Predictive Control (MPC) scheme is employed for controlling the Capture Point (CP) to a desired position by modulating both the ZMP and the Centroidal Moment Pivot (CMP). The goal of the proposed controller is to control the CP, employing the CMP when the CP is out of the support polygon, and/or the ZMP when the CP is inside the support polygon. The proposed algorithm is implemented on an abstract model of the SURENA III humanoid robot. Obtained results show the effectiveness of the proposed approach in the presence of severe pushes, even when the support polygon is shrunken to a point or a line.
IEEE/RSI International Conference on Robotics and Mechatronics (ICROM)

Clearance imposes some uncontrollable degrees of freedom to a manipulator. Therefore, poor dynamic performance, low accuracy, reduction in components lifetime and generation of undesirable vibrations result in the impacts of mating parts in a clearance joint. In this study, the effects of clearance on the dynamics of a planar 3-RRR parallel manipulator are investigated. Then, an optimization algorithm for simultaneously kinematic and dynamic synthesis has been carried out to reduce these effects and improve the performance and the accuracy of the manipulator. The algorithm is based on changing the lengths and the mass distribution of the links. By combining the Lagrange equations with Lankarani–Nikravesh contact force model, a series of dynamic equations are established. Then, the highly nonlinear optimization problem is tackled via a PSO method. The last but not the least, the efficiency of the algorithm and its superiority have been demonstrated by a numerical example. We claim that the linear and angular accelerations of the links and the contact forces in the joints are bounded and evolve very smoothly in the optimal design. Finally, to verify the validity of the optimization algorithm, the planar 3-RRR parallel manipulator is modeled in MSC.ADAMS software and the simulation results are compared for the original and the optimal manipulators.
Nonlinear Dynamics