A reactive controller framework for quadrupedal locomotion on challenging terrain
By Victor Barasuol, Jonas Buchli, Claudio Semini, Marco Frigerio, E. D. Pieri, Darwin G. Caldwell. 2013
199 citations recorded by the source corpus.
Subfields: Biomedical Engineering, Control and Systems Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biomedical Engineering
- Sangbae Kim
- Gerardo Bledt
- Patrick M. Wensing
- José Santos-Victor
- Darwin G. Caldwell
- Sébastien Gay
- Auke Jan Ijspeert
- Benjamin Katz
In Control and Systems Engineering
- José Santos-Victor
- Darwin G. Caldwell
- Claudio Semini
- Michele Focchi
- Sangbae Kim
- Carlos Mastalli
- Victor Barasuol
- Romeo Orsolino
Citations in the corpus
- Learning quadrupedal locomotion over challenging terrain (2020, neutral): “The target foot positions, which are the output of the motion generation block, are defined in the horizontal frames [35] of the feet (Hi, i ∈ {1, 2, 3, 4}).”
- Contact Model Fusion for Event-Based Locomotion in Unstructured Terrains (2018, background): “Reactively modified central pattern generators (CPGs) have also shown an ability for terrain adaptability [9]; however, the kinematic nature of CPGs requires additional mechanisms to manage dynamic balance.”
- Heuristic Planning for Rough Terrain Locomotion in Presence of External Disturbances and Variable Perception Quality (2018, background): “ser to be coplanar. In this case, the influence of the “outlier” foot (e.g. the one on the pallet) would be reduced. For more dynamic gaits, where the CoM trajectory is not planned (e.g. like trotting [23]), a low-pass filtering of the terrain estimate would be sufficient to mitigate the effect of the ”
- A Review of Algorithms for Compliant Control of Stiff and Fixed-Compliance Robots (2016, background): “For these reasons, literature examples of impedance control are more recent than admittance implementations [55]–[59].”
- Learning robot gait stability using neural networks as sensory feedback function for Central Pattern Generators (2013, methodology): “In [4] the authors use a kinematic model of the robot to adjust the foot locus according to the robot orientation, and inverse dynamics to stabilize it on challenging terrain.”