4,198 research outputs found
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
A Benchmarking of DCM Based Architectures for Position and Velocity Controlled Walking of Humanoid Robots
This paper contributes towards the development and comparison of
Divergent-Component-of-Motion (DCM) based control architectures for humanoid
robot locomotion. More precisely, we present and compare several DCM based
implementations of a three layer control architecture. From top to bottom,
these three layers are here called: trajectory optimization, simplified model
control, and whole-body QP control. All layers use the DCM concept to generate
references for the layer below. For the simplified model control layer, we
present and compare both instantaneous and Receding Horizon Control
controllers. For the whole-body QP control layer, we present and compare
controllers for position and velocity control robots. Experimental results are
carried out on the one-meter tall iCub humanoid robot. We show which
implementation of the above control architecture allows the robot to achieve a
walking velocity of 0.41 meters per second.Comment: Submitted to Humanoids201
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
When and Where to Step: Terrain-Aware Real-Time Footstep Location and Timing Optimization for Bipedal Robots
Online footstep planning is essential for bipedal walking robots, allowing
them to walk in the presence of disturbances and sensory noise. Most of the
literature on the topic has focused on optimizing the footstep placement while
keeping the step timing constant. In this work, we introduce a footstep planner
capable of optimizing footstep placement and step time online. The proposed
planner, consisting of an Interior Point Optimizer (IPOPT) and an optimizer
based on Augmented Lagrangian (AL) method with analytical gradient descent,
solves the full dynamics of the Linear Inverted Pendulum (LIP) model in real
time to optimize for footstep location as well as step timing at the rate of
200~Hz. We show that such asynchronous real-time optimization with the AL
method (ARTO-AL) provides the required robustness and speed for successful
online footstep planning. Furthermore, ARTO-AL can be extended to plan
footsteps in 3D, allowing terrain-aware footstep planning on uneven terrains.
Compared to an algorithm with no footstep time adaptation, our proposed ARTO-AL
demonstrates increased stability in simulated walking experiments as it can
resist pushes on flat ground and on a ramp up to 120 N and 100 N
respectively. For the video, see https://youtu.be/ABdnvPqCUu4. For code, see
https://github.com/WangKeAlchemist/ARTO-AL/tree/master.Comment: 32 pages, 15 figures. Submitted to Robotics and Autonomous System
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