690 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
In silico case studies of compliant robots: AMARSI deliverable 3.3
In the deliverable 3.2 we presented how the morphological computing ap-
proach can significantly facilitate the control strategy in several scenarios,
e.g. quadruped locomotion, bipedal locomotion and reaching. In particular,
the Kitty experimental platform is an example of the use of morphological
computation to allow quadruped locomotion. In this deliverable we continue
with the simulation studies on the application of the different morphological
computation strategies to control a robotic system
Climbing favours the tripod gait over alternative faster insect gaits.
To escape danger or catch prey, running vertebrates rely on dynamic gaits with minimal ground contact. By contrast, most insects use a tripod gait that maintains at least three legs on the ground at any given time. One prevailing hypothesis for this difference in fast locomotor strategies is that tripod locomotion allows insects to rapidly navigate three-dimensional terrain. To test this, we computationally discovered fast locomotor gaits for a model based on Drosophila melanogaster. Indeed, the tripod gait emerges to the exclusion of many other possible gaits when optimizing fast upward climbing with leg adhesion. By contrast, novel two-legged bipod gaits are fastest on flat terrain without adhesion in the model and in a hexapod robot. Intriguingly, when adhesive leg structures in real Drosophila are covered, animals exhibit atypical bipod-like leg coordination. We propose that the requirement to climb vertical terrain may drive the prevalence of the tripod gait over faster alternative gaits with minimal ground contact
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