Legged Locomotion: Combining Control Strategies to Increase Performance
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1
Georg-August Universitaet Goettingen, Germany
Walking animals achieve efficient locomotion in rough terrains and complex environments.
To date, most walking robots employ preprogrammed reactive control leading to a limitation of adaptivity. As a consequence, they might have difficulty to locomote over unknown rough terrain. We are interested in the effect of changing control strategies for some degrees of freedom (DOFs) on locomotion of a hexapod robot in rough terrain. This is investigated by replacing the central pattern generator driven control by less regular strategies.
Three different control strategies are evaluated: a simple neural central pattern generator (PureCPG) [1], a combination of the PureCPG with random walk (RWC) and a combination of the PureCPG with homeokinesis [2] (HKC). In the first case all DOFs are controlled by PureCPG. In the second and third cases the coxa-trochanteral (CTr) joints for elevation and depression of the front legs are controlled by RWC and HKC respectively. Performance is measured in terms of distance travelled in body lengths.
The platform used is a simulation of the real hexapod robot AMOSII. In the experiments we place the robot into rough terrain containing plateaus of three higher altitudes. The maximum altitude being roughly 1.4 times the maximum ground clearance of the legs. Note that the maximum ground clearance of all controllers is equal due to identically chosen joint limits. With these joint limits the PureCPG controller generates an exaggerated stalking gait, already adapted to the rough terrain.
Our results show a clearly higher performance of HKC and RWC compared to the PureCPG.
The PureCPG performs a periodic pattern that, once stuck for one cycle, barely has a chance to gain any more ground. The other controllers can vary the position of the front CTr joints relative to the other DOFs. Therefore they have a greater behavioral repertoire to traverse the terrain.
Furthermore, we observe that the RWC and the HKC do not perform significantly different (however, the HKC looks slightly better). So random behavior seems like quite a good strategy in rough terrain. Nonetheless there are advantages to the use of feedback from the sensors which are exploited by the HKC without performance loss. An example is reduced strain on the material as shown by [3].
Acknowledgements
This research was supported by the BMBF-funded BFNT Göttingen with grant numbers 01GQ0811 (project 3B) and BCCN Göttingen with grant number 01GQ1005A (project D1) and the Emmy Noether Program (DFG, MA4464/3-1).
References
[1] Manoonpong P., Woegoetter F., Pasemann F. (2010) Biological Inspiration for Mechanical Design and Control of Autonomous Walking Robots: Towards Life-like Robots. The International Journal of Applied Biomedical Engineering (IJABME), Vol. 3, No. 1, pp. 1-12.
[2] Der, Ralf. 2001. Self-organized acquisition of situated behaviors. Theory in Biosciences 120, no. 3-4: 179-187.
[3] Hesse F., Manoonpong P., Wörgötter F. (2011) Neuronal Feedback Guiding Motor Command Execution, BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011. Submitted.
Keywords:
central pattern generator,
CpG,
hexapod,
Homeokinesis,
Locomotion,
Robotics,
Sensor motor systems
Conference:
BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011, Freiburg, Germany, 4 Oct - 6 Oct, 2011.
Presentation Type:
Poster
Topic:
motor control (please use "motor control" as keyword)
Citation:
Biehl
M,
Manoonpong
P,
Hesse
F and
Woergoetter
F
(2011). Legged Locomotion: Combining Control Strategies to Increase Performance.
Front. Comput. Neurosci.
Conference Abstract:
BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011.
doi: 10.3389/conf.fncom.2011.53.00087
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Received:
23 Aug 2011;
Published Online:
04 Oct 2011.
*
Correspondence:
Mr. Martin Biehl, Georg-August Universitaet Goettingen, Goettingen, Germany, mab@physik3.gwdg.de