1,071 research outputs found
Momentum Control with Hierarchical Inverse Dynamics on a Torque-Controlled Humanoid
Hierarchical inverse dynamics based on cascades of quadratic programs have
been proposed for the control of legged robots. They have important benefits
but to the best of our knowledge have never been implemented on a torque
controlled humanoid where model inaccuracies, sensor noise and real-time
computation requirements can be problematic. Using a reformulation of existing
algorithms, we propose a simplification of the problem that allows to achieve
real-time control. Momentum-based control is integrated in the task hierarchy
and a LQR design approach is used to compute the desired associated closed-loop
behavior and improve performance. Extensive experiments on various balancing
and tracking tasks show very robust performance in the face of unknown
disturbances, even when the humanoid is standing on one foot. Our results
demonstrate that hierarchical inverse dynamics together with momentum control
can be efficiently used for feedback control under real robot conditions.Comment: 21 pages, 11 figures, 4 tables in Autonomous Robots (2015
Straight-Leg Walking Through Underconstrained Whole-Body Control
We present an approach for achieving a natural, efficient gait on bipedal
robots using straightened legs and toe-off. Our algorithm avoids complex height
planning by allowing a whole-body controller to determine the straightest
possible leg configuration at run-time. The controller solutions are biased
towards a straight leg configuration by projecting leg joint angle objectives
into the null-space of the other quadratic program motion objectives. To allow
the legs to remain straight throughout the gait, toe-off was utilized to
increase the kinematic reachability of the legs. The toe-off motion is achieved
through underconstraining the foot position, allowing it to emerge naturally.
We applied this approach of under-specifying the motion objectives to the Atlas
humanoid, allowing it to walk over a variety of terrain. We present both
experimental and simulation results and discuss performance limitations and
potential improvements.Comment: Submitted to 2018 IEEE International Conference on Robotics and
Automatio
Trajectory generation for multi-contact momentum-control
Simplified models of the dynamics such as the linear inverted pendulum model
(LIPM) have proven to perform well for biped walking on flat ground. However,
for more complex tasks the assumptions of these models can become limiting. For
example, the LIPM does not allow for the control of contact forces
independently, is limited to co-planar contacts and assumes that the angular
momentum is zero. In this paper, we propose to use the full momentum equations
of a humanoid robot in a trajectory optimization framework to plan its center
of mass, linear and angular momentum trajectories. The model also allows for
planning desired contact forces for each end-effector in arbitrary contact
locations. We extend our previous results on LQR design for momentum control by
computing the (linearized) optimal momentum feedback law in a receding horizon
fashion. The resulting desired momentum and the associated feedback law are
then used in a hierarchical whole body control approach. Simulation experiments
show that the approach is computationally fast and is able to generate plans
for locomotion on complex terrains while demonstrating good tracking
performance for the full humanoid control
Motion Planning and Control of Dynamic Humanoid Locomotion
Inspired by human, humanoid robots has the potential to become a general-purpose platform that lives along with human. Due to the technological advances in many field, such as actuation, sensing, control and intelligence, it finally enables humanoid robots to possess human comparable capabilities. However, humanoid locomotion is still a challenging research field. The large number of degree of freedom structure makes the system difficult to coordinate online. The presence of various contact constraints and the hybrid nature of locomotion tasks make the planning a harder problem to solve. Template model anchoring approach has been adopted to bridge the gap between simple model behavior and the whole-body motion of humanoid robot.
Control policies are first developed for simple template models like Linear Inverted Pendulum Model (LIPM) or Spring Loaded Inverted Pendulum(SLIP), the result controlled behaviors are then been mapped to the whole-body motion of humanoid robot through optimization-based task-space control strategies. Whole-body humanoid control framework has been verified on various contact situations such as unknown uneven terrain, multi-contact scenarios and moving platform and shows its generality and versatility. For walking motion, existing Model Predictive Control approach based on LIPM has been extended to enable the robot to walk without any reference foot placement anchoring. It is kind of discrete version of \u201cwalking without thinking\u201d.
As a result, the robot could achieve versatile locomotion modes such as automatic foot placement with single reference velocity command, reactive stepping under large external disturbances, guided walking with small constant external pushing forces, robust walking on unknown uneven terrain, reactive stepping in place when blocked by external barrier. As an extension of this proposed framework, also to increase the push recovery capability of the humanoid robot, two new configurations have been proposed to enable the robot to perform cross-step motions. For more dynamic hopping and running motion, SLIP model has been chosen as the template model. Different from traditional model-based analytical approach, a data-driven approach has been proposed to encode the dynamics of the this model. A deep neural network is trained offline with a large amount of simulation data based on the SLIP model to learn its dynamics.
The trained network is applied online to generate reference foot placements for the humanoid robot. Simulations have been performed to evaluate the effectiveness of the proposed approach in generating bio-inspired and robust running motions. The method proposed based on 2D SLIP model can be generalized to 3D SLIP model and the extension has been briefly mentioned at the end
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
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