2,189 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
Multi-contact Walking Pattern Generation based on Model Preview Control of 3D COM Accelerations
We present a multi-contact walking pattern generator based on preview-control
of the 3D acceleration of the center of mass (COM). A key point in the design
of our algorithm is the calculation of contact-stability constraints. Thanks to
a mathematical observation on the algebraic nature of the frictional wrench
cone, we show that the 3D volume of feasible COM accelerations is a always a
downward-pointing cone. We reduce its computation to a convex hull of (dual) 2D
points, for which optimal O(n log n) algorithms are readily available. This
reformulation brings a significant speedup compared to previous methods, which
allows us to compute time-varying contact-stability criteria fast enough for
the control loop. Next, we propose a conservative trajectory-wide
contact-stability criterion, which can be derived from COM-acceleration volumes
at marginal cost and directly applied in a model-predictive controller. We
finally implement this pipeline and exemplify it with the HRP-4 humanoid model
in multi-contact dynamically walking scenarios
IndoorSim-to-OutdoorReal: Learning to Navigate Outdoors without any Outdoor Experience
We present IndoorSim-to-OutdoorReal (I2O), an end-to-end learned visual
navigation approach, trained solely in simulated short-range indoor
environments, and demonstrates zero-shot sim-to-real transfer to the outdoors
for long-range navigation on the Spot robot. Our method uses zero real-world
experience (indoor or outdoor), and requires the simulator to model no
predominantly-outdoor phenomenon (sloped grounds, sidewalks, etc). The key to
I2O transfer is in providing the robot with additional context of the
environment (i.e., a satellite map, a rough sketch of a map by a human, etc.)
to guide the robot's navigation in the real-world. The provided context-maps do
not need to be accurate or complete -- real-world obstacles (e.g., trees,
bushes, pedestrians, etc.) are not drawn on the map, and openings are not
aligned with where they are in the real-world. Crucially, these inaccurate
context-maps provide a hint to the robot about a route to take to the goal. We
find that our method that leverages Context-Maps is able to successfully
navigate hundreds of meters in novel environments, avoiding novel obstacles on
its path, to a distant goal without a single collision or human intervention.
In comparison, policies without the additional context fail completely. Lastly,
we test the robustness of the Context-Map policy by adding varying degrees of
noise to the map in simulation. We find that the Context-Map policy is
surprisingly robust to noise in the provided context-map. In the presence of
significantly inaccurate maps (corrupted with 50% noise, or entirely blank
maps), the policy gracefully regresses to the behavior of a policy with no
context. Videos are available at https://www.joannetruong.com/projects/i2o.htm
Development of a Quadruped Robot and Parameterized Stair-Climbing Behavior
Stair-climbing is a difficult task for mobile robots to accomplish, particularly for legged robots. While quadruped robots have previously demonstrated the ability to climb stairs, none have so far been capable of climbing stairs of variable height while carrying all required sensors, controllers, and power sources on-board. The goal of this thesis was the development of a self-contained quadruped robot capable of detecting, classifying, and climbing stairs of any height within a specified range. The design process for this robot is described, including the development of the joint, leg, and body configuration, the design and selection of components, and both dynamic and finite element analyses performed to verify the design. A parameterized stair-climbing gait is then developed, which is adaptable to any stair height of known width and height. This behavior is then implemented on the previously discussed quadruped robot, which then demonstrates the capability to climb three different stair variations with no configuration change
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