21 research outputs found
Fast and Continuous Foothold Adaptation for Dynamic Locomotion through CNNs
Legged robots can outperform wheeled machines for most navigation tasks
across unknown and rough terrains. For such tasks, visual feedback is a
fundamental asset to provide robots with terrain-awareness. However, robust
dynamic locomotion on difficult terrains with real-time performance guarantees
remains a challenge. We present here a real-time, dynamic foothold adaptation
strategy based on visual feedback. Our method adjusts the landing position of
the feet in a fully reactive manner, using only on-board computers and sensors.
The correction is computed and executed continuously along the swing phase
trajectory of each leg. To efficiently adapt the landing position, we implement
a self-supervised foothold classifier based on a Convolutional Neural Network
(CNN). Our method results in an up to 200 times faster computation with respect
to the full-blown heuristics. Our goal is to react to visual stimuli from the
environment, bridging the gap between blind reactive locomotion and purely
vision-based planning strategies. We assess the performance of our method on
the dynamic quadruped robot HyQ, executing static and dynamic gaits (at speeds
up to 0.5 m/s) in both simulated and real scenarios; the benefit of safe
foothold adaptation is clearly demonstrated by the overall robot behavior.Comment: 9 pages, 11 figures. Accepted to RA-L + ICRA 2019, January 201
Fast and Continuous Foothold Adaptation for Dynamic Locomotion Through CNNs
Legged robots can outperform wheeled machines for most navigation tasks across unknown and rough terrains. For such tasks, visual feedback is a fundamental asset to provide robots with terrain awareness. However, robust dynamic locomotion on difficult terrains with real-time performance guarantees remains a challenge. We present here a real-time, dynamic foothold adaptation strategy based on visual feedback. Our method adjusts the landing position of the feet in a fully reactive manner, using only on-board computers and sensors. The correction is computed and executed continuously along the swing phase trajectory of each leg. To efficiently adapt the landing position, we implement a self-supervised foothold classifier based on a convolutional neural network. Our method results in an up to 200 times faster computation with respect to the full-blown heuristics. Our goal is to react to visual stimuli from the environment, bridging the gap between blind reactive locomotion and purely vision-based planning strategies. We assess the performance of our method on the dynamic quadruped robot HyQ, executing static and dynamic gaits (at speeds up to 0.5 m/s) in both simulated and real scenarios; the benefit of safe foothold adaptation is clearly demonstrated by the overall robot behavior
Bridging Vision and Dynamic Legged Locomotion
Legged robots have demonstrated remarkable advances regarding robustness and versatility in the past decades. The questions that need to be addressed in this field are increasingly focusing on reasoning about the environment and autonomy rather than locomotion only. To answer some of these questions visual information is essential. If a robot has information about the terrain it can plan and take preventive actions against potential risks. However, building a model of the terrain is often computationally costly, mainly because of the dense nature of visual data. On top of the mapping problem, robots need feasible body trajectories and contact sequences to traverse the terrain safely, which may also require heavy computations. This computational cost has limited the use of visual feedback to contexts that guarantee (quasi-) static stability, or resort to planning schemes where contact sequences and body trajectories are computed before starting to execute motions. In this thesis we propose a set of algorithms that reduces the gap between visual processing and dynamic locomotion. We use machine learning to speed up visual data processing and model predictive control to achieve locomotion robustness. In particular, we devise a novel foothold adaptation strategy that uses a map of the terrain built from on-board vision sensors. This map is sent to a foothold classifier based on a convolutional neural network that allows the robot to adjust the landing position of the feet in a fast and continuous fashion. We then use the convolutional neural network-based classifier to provide safe future contact sequences to a model predictive controller that optimizes target ground reaction forces in order to track a desired center of mass trajectory. We perform simulations and experiments on the hydraulic quadruped robots HyQ and HyQReal. For all experiments the contact sequences, the foothold adaptations, the control inputs and the map are computed and processed entirely on-board. The various tests show that the robot is able to leverage the visual terrain information to handle complex scenarios in a safe, robust and reliable manner
SafeSteps: Learning Safer Footstep Planning Policies for Legged Robots via Model-Based Priors
We present a footstep planning policy for quadrupedal locomotion that is able
to directly take into consideration a-priori safety information in its
decisions. At its core, a learning process analyzes terrain patches,
classifying each landing location by its kinematic feasibility, shin collision,
and terrain roughness. This information is then encoded into a small vector
representation and passed as an additional state to the footstep planning
policy, which furthermore proposes only safe footstep location by applying a
masked variant of the Proximal Policy Optimization (PPO) algorithm. The
performance of the proposed approach is shown by comparative simulations on an
electric quadruped robot walking in different rough terrain scenarios. We show
that violations of the above safety conditions are greatly reduced both during
training and the successive deployment of the policy, resulting in an
inherently safer footstep planner. Furthermore, we show how, as a byproduct,
fewer reward terms are needed to shape the behavior of the policy, which in
return is able to achieve both better final performances and sample efficienc
Hierarchical Experience-informed Navigation for Multi-modal Quadrupedal Rebar Grid Traversal
This study focuses on a layered, experience-based, multi-modal contact
planning framework for agile quadrupedal locomotion over a constrained rebar
environment. To this end, our hierarchical planner incorporates
locomotion-specific modules into the high-level contact sequence planner and
solves kinodynamically-aware trajectory optimization as the low-level motion
planner. Through quantitative analysis of the experience accumulation process
and experimental validation of the kinodynamic feasibility of the generated
locomotion trajectories, we demonstrate that the experience planning heuristic
offers an effective way of providing candidate footholds for a legged contact
planner. Additionally, we introduce a guiding torso path heuristic at the
global planning level to enhance the navigation success rate in the presence of
environmental obstacles. Our results indicate that the torso-path guided
experience accumulation requires significantly fewer offline trials to
successfully reach the goal compared to regular experience accumulation.
Finally, our planning framework is validated in both dynamics simulations and
real hardware implementations on a quadrupedal robot provided by Skymul Inc
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
Model Predictive Control With Environment Adaptation for Legged Locomotion
Re-planning in legged locomotion is crucial to track the desired user
velocity while adapting to the terrain and rejecting external disturbances. In
this work, we propose and test in experiments a real-time Nonlinear Model
Predictive Control (NMPC) tailored to a legged robot for achieving dynamic
locomotion on a variety of terrains. We introduce a mobility-based criterion to
define an NMPC cost that enhances the locomotion of quadruped robots while
maximizing leg mobility and improves adaptation to the terrain features. Our
NMPC is based on the real-time iteration scheme that allows us to re-plan
online at with a prediction horizon of seconds. We use
the single rigid body dynamic model defined in the center of mass frame in
order to increase the computational efficiency. In simulations, the NMPC is
tested to traverse a set of pallets of different sizes, to walk into a V-shaped
chimney,and to locomote over rough terrain. In real experiments, we demonstrate
the effectiveness of our NMPC with the mobility feature that allowed IIT's
quadruped robot HyQ to achieve an omni-directional walk on
flat terrain, to traverse a static pallet, and to adapt to a repositioned
pallet during a walk.Comment: Video available on: https://youtu.be/r0-KIiw0eW
Robust Legged Robot State Estimation Using Factor Graph Optimization
Legged robots, specifically quadrupeds, are becoming increasingly attractive
for industrial applications such as inspection. However, to leave the
laboratory and to become useful to an end user requires reliability in harsh
conditions. From the perspective of state estimation, it is essential to be
able to accurately estimate the robot's state despite challenges such as uneven
or slippery terrain, textureless and reflective scenes, as well as dynamic
camera occlusions. We are motivated to reduce the dependency on foot contact
classifications, which fail when slipping, and to reduce position drift during
dynamic motions such as trotting. To this end, we present a factor graph
optimization method for state estimation which tightly fuses and smooths
inertial navigation, leg odometry and visual odometry. The effectiveness of the
approach is demonstrated using the ANYmal quadruped robot navigating in a
realistic outdoor industrial environment. This experiment included trotting,
walking, crossing obstacles and ascending a staircase. The proposed approach
decreased the relative position error by up to 55% and absolute position error
by 76% compared to kinematic-inertial odometry.Comment: 8 pages, 12 figures. Accepted to RA-L + IROS 2019, July 201