8 research outputs found
Motion Planning and Control for the Locomotion of Humanoid Robot
This thesis aims to contribute on the motion planning and control problem of the locomotion
of humanoid robots. For the motion planning, various methods were proposed
in different levels of model dependence. First, a model free approach was proposed
which utilizes linear regression to estimate the relationship between foot placement
and moving velocity. The data-based feature makes it quite robust to handle modeling
error and external disturbance. As a generic control philosophy, it can be applied to
various robots with different gaits. To reduce the risk of collecting experimental data
of model-free method, based on the simplified linear inverted pendulum model, the
classic planning method of model predictive control was explored to optimize CoM
trajectory with predefined foot placements or optimize them two together with respect
to the ZMP constraint. Along with elaborately designed re-planning algorithm and
sparse discretization of trajectories, it is fast enough to run in real time and robust
enough to resist external disturbance. Thereafter, nonlinear models are utilized for
motion planning by performing forward simulation iteratively following the multiple
shooting method. A walking pattern is predefined to fix most of the degrees of the
robot, and only one decision variable, foot placement, is left in one motion plane and
therefore able to be solved in milliseconds which is sufficient to run in real time. In
order to track the planned trajectories and prevent the robot from falling over, diverse
control strategies were proposed according to the types of joint actuators. CoM stabilizer
was designed for the robots with position-controlled joints while quasi-static
Cartesian impedance control and optimization-based full body torque control were
implemented for the robots with torque-controlled joints. Various scenarios were set
up to demonstrate the feasibility and robustness of the proposed approaches, like
walking on uneven terrain, walking with narrow feet or straight leg, push recovery
and so on
Contact-Implicit Trajectory Optimization using an Analytically Solvable Contact Model for Locomotion on Variable Ground
This paper presents a novel contact-implicit trajectory optimization method
using an analytically solvable contact model to enable planning of interactions
with hard, soft, and slippery environments. Specifically, we propose a novel
contact model that can be computed in closed-form, satisfies friction cone
constraints and can be embedded into direct trajectory optimization frameworks
without complementarity constraints. The closed-form solution decouples the
computation of the contact forces from other actuation forces and this property
is used to formulate a minimal direct optimization problem expressed with
configuration variables only. Our simulation study demonstrates the advantages
over the rigid contact model and a trajectory optimization approach based on
complementarity constraints. The proposed model enables physics-based
optimization for a wide range of interactions with hard, slippery, and soft
grounds in a unified manner expressed by two parameters only. By computing
trotting and jumping motions for a quadruped robot, the proposed optimization
demonstrates the versatility for multi-contact motion planning on surfaces with
different physical properties.Comment: in IEEE Robotics and Automation Letter
A Study of Nonlinear Forward Models for Dynamic Walking
You Y, Zhou C, Li Z, Tsagarakis N. A Study of Nonlinear Forward Models for Dynamic Walking. In: IEEE International Conference on Robotics and Automation (ICRA). Singapore; Accepted
Straight Leg Walking Strategy for Torque-controlled Humanoid Robots
You Y, Xin S, Zhou C, Tsagarakis N. Straight Leg Walking Strategy for Torque-controlled Humanoid Robots. In: IEEE International Conference on Robotics and Biomimetics. Qingdao, China; 2016: 2014-2019
Locomotion Adaptation in Heavy Payload Transportation Tasks with the Quadruped Robot CENTAURO
International audienceThis paper presents a reactive legged locomotion generation scheme that enables our quadruped robot CEN-TAURO to adapt to varying payloads while walking. The center-of-mass (CoM) trajectories are generated in real time in a model predictive control (MPC) fashion, trading off large stability margins against evenly stretched legs. Vertexbased zero-moment-point (ZMP) constraints are imposed to ensure quasi-static walking stability. A Kalman filter is then implemented to estimate the CoM states and the impact of external payloads which can vary online and affect/disturb the locomotion differently. The CoM estimation is used to update the MPC motion planner at every replanning instant so that the robot can react to unknown and time-varying payloads on the fly. We validate the proposed scheme through experimental trials where the robot walks on flat ground or steps on different surface levels while carrying heavy payloads. It is shown that the proposed reactive locomotion strategy enables the robot to carry 20 kg payloads, which is close to the maximum capacity of the robot arms