1,551 research outputs found
An Optimized Linear Model Predictive Control Solver for Online Walking Motion Generation
International audienceThis article addresses the fast solution of a Quadratic Program underlying a Linear Model Predictive Control scheme that generates walking motions. We introduce an algorithm which is tailored to the particular requirements of this problem, and therefore able to solve it efficiently. Different aspects of the algorithm are examined, its computational complexity is presented, and a numerical comparison with an existing state of the art solver is made. The approach presented here, extends to other general problems in a straightforward way
Real-Time Motion Planning of Legged Robots: A Model Predictive Control Approach
We introduce a real-time, constrained, nonlinear Model Predictive Control for
the motion planning of legged robots. The proposed approach uses a constrained
optimal control algorithm known as SLQ. We improve the efficiency of this
algorithm by introducing a multi-processing scheme for estimating value
function in its backward pass. This pass has been often calculated as a single
process. This parallel SLQ algorithm can optimize longer time horizons without
proportional increase in its computation time. Thus, our MPC algorithm can
generate optimized trajectories for the next few phases of the motion within
only a few milliseconds. This outperforms the state of the art by at least one
order of magnitude. The performance of the approach is validated on a quadruped
robot for generating dynamic gaits such as trotting.Comment: 8 page
Frequency-Aware Model Predictive Control
Transferring solutions found by trajectory optimization to robotic hardware
remains a challenging task. When the optimization fully exploits the provided
model to perform dynamic tasks, the presence of unmodeled dynamics renders the
motion infeasible on the real system. Model errors can be a result of model
simplifications, but also naturally arise when deploying the robot in
unstructured and nondeterministic environments. Predominantly, compliant
contacts and actuator dynamics lead to bandwidth limitations. While classical
control methods provide tools to synthesize controllers that are robust to a
class of model errors, such a notion is missing in modern trajectory
optimization, which is solved in the time domain. We propose frequency-shaped
cost functions to achieve robust solutions in the context of optimal control
for legged robots. Through simulation and hardware experiments we show that
motion plans can be made compatible with bandwidth limits set by actuators and
contact dynamics. The smoothness of the model predictive solutions can be
continuously tuned without compromising the feasibility of the problem.
Experiments with the quadrupedal robot ANYmal, which is driven by
highly-compliant series elastic actuators, showed significantly improved
tracking performance of the planned motion, torque, and force trajectories and
enabled the machine to walk robustly on terrain with unmodeled compliance
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
Walking Stabilization Using Step Timing and Location Adjustment on the Humanoid Robot, Atlas
While humans are highly capable of recovering from external disturbances and
uncertainties that result in large tracking errors, humanoid robots have yet to
reliably mimic this level of robustness. Essential to this is the ability to
combine traditional "ankle strategy" balancing with step timing and location
adjustment techniques. In doing so, the robot is able to step quickly to the
necessary location to continue walking. In this work, we present both a new
swing speed up algorithm to adjust the step timing, allowing the robot to set
the foot down more quickly to recover from errors in the direction of the
current capture point dynamics, and a new algorithm to adjust the desired
footstep, expanding the base of support to utilize the center of pressure
(CoP)-based ankle strategy for balance. We then utilize the desired centroidal
moment pivot (CMP) to calculate the momentum rate of change for our
inverse-dynamics based whole-body controller. We present simulation and
experimental results using this work, and discuss performance limitations and
potential improvements
Online Optimization-based Gait Adaptation of Quadruped Robot Locomotion
Quadruped robots demonstrated extensive capabilities of traversing complex and unstructured
environments. Optimization-based techniques gave a relevant impulse to the research on legged
locomotion. Indeed, by designing the cost function and the constraints, we can guarantee the
feasibility of a motion and impose high-level locomotion tasks, e.g., tracking of a reference
velocity. This allows one to have a generic planning approach without the need to tailor a
specific motion for each terrain, as in the heuristic case. In this context, Model Predictive
Control (MPC) can compensate for model inaccuracies and external disturbances, thanks to
the high-frequency replanning.
The main objective of this dissertation is to develop a Nonlinear MPC (NMPC)-based
locomotion framework for quadruped robots. The aim is to obtain an algorithm which can
be extended to different robots and gaits; in addition, I sought to remove some assumptions
generally done in the literature, e.g., heuristic reference generator and user-defined gait
sequence.
The starting point of my work is the definition of the Optimal Control Problem to generate
feasible trajectories for the Center of Mass. It is descriptive enough to capture the linear and
angular dynamics of the robot as a whole. A simplified model (Single Rigid Body Dynamics
model) is used for the system dynamics, while a novel cost term maximizes leg mobility
to improve robustness in the presence of nonflat terrain. In addition, to test the approach
on the real robot, I dedicated particular effort to implementing both a heuristic reference
generator and an interface for the controller, and integrating them into the controller framework
developed previously by other team members.
As a second contribution of my work, I extended the locomotion framework to deal with a
trot gait. In particular, I generalized the reference generator to be based on optimization.
Exploiting the Linear Inverted Pendulum model, this new module can deal with the underactuation of the trot when only two legs are in contact with the ground, endowing the NMPC
with physically informed reference trajectories to be tracked. In addition, the reference velocities are used to correct the heuristic footholds, obtaining contact locations coherent with
the motion of the base, even though they are not directly optimized.
The model used by the NMPC receives as input the gait sequence, thus with the last part
of my work I developed an online multi-contact planner and integrated it into the MPC
framework. Using a machine learning approach, the planner computes the best feasible option,
even in complex environments, in a few milliseconds, by ranking online a set of discrete options
for footholds, i.e., which leg to move and where to step. To train the network, I designed
a novel function, evaluated offline, which considers the value of the cost of the NMPC and
robustness/stability metrics for each option.
These methods have been validated with simulations and experiments over the three years. I
tested the NMPC on the Hydraulically actuated Quadruped robot (HyQ) of the IIT’s Dynamic
Legged Systems lab, performing omni-directional motions on flat terrain and stepping on
a pallet (both static and relocated during the motion) with a crawl gait. The trajectory
replanning is performed at high-frequency, and visual information of the terrain is included to
traverse uneven terrain. A Unitree Aliengo quadruped robot is used to execute experiments
with the trot gait. The optimization-based reference generator allows the robot to reach a
fixed goal and recover from external pushes without modifying the structure of the NMPC.
Finally, simulations with the Solo robot are performed to validate the neural network-based
contact planning. The robot successfully traverses complex scenarios, e.g., stepping stones,
with both walk and trot gaits, choosing the footholds online.
The achieved results improved the robustness and the performance of the quadruped locomotion.
High-frequency replanning, dealing with a fixed goal, recovering after a push, and the automatic
selection of footholds could help the robots to accomplish important tasks for the humans,
for example, providing support in a disaster response scenario or inspecting an unknown
environment.
In the future, the contact planning will be transferred to the real hardware. Possible developments foresee the optimization of the gait timings, i.e., stance and swing duration, and a
framework which allows the automatic transition between gaits
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