| Title |
Bipedal robotic walking control derived from analysis of human locomotion
|
|---|---|
| Published in |
Biological Cybernetics, February 2018
|
| DOI | 10.1007/s00422-018-0750-5 |
| Pubmed ID | |
| Authors |
Lin Meng, Catherine A. Macleod, Bernd Porr, Henrik Gollee |
| Abstract |
This paper proposes the design of a bipedal robotic controller where the function between the sensory input and motor output is treated as a black box derived from human data. In order to achieve this, we investigated the causal relationship between ground contact information from the feet and leg muscle activity n human walking and calculated filter functions which transform sensory signals to motor actions. A minimal, nonlinear, and robust control system was created and subsequently analysed by applying it to our bipedal robot RunBot III without any central pattern generators or precise trajectory control. The results demonstrate that our controller can generate stable robotic walking. This indicates that complex locomotion patterns can result from a simple model based on reflexes and supports the premise that human-derived control strategies have potential applications in robotics or assistive devices. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 48 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 10 | 21% |
| Student > Bachelor | 8 | 17% |
| Other | 5 | 10% |
| Researcher | 4 | 8% |
| Student > Master | 4 | 8% |
| Other | 8 | 17% |
| Unknown | 9 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 16 | 33% |
| Medicine and Dentistry | 5 | 10% |
| Nursing and Health Professions | 4 | 8% |
| Sports and Recreations | 3 | 6% |
| Neuroscience | 3 | 6% |
| Other | 9 | 19% |
| Unknown | 8 | 17% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 February 2018.
All research outputs
#17,286,645
of 25,374,917 outputs
Outputs from Biological Cybernetics
#495
of 678 outputs
Outputs of similar age
#282,104
of 445,916 outputs
Outputs of similar age from Biological Cybernetics
#6
of 6 outputs
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