Kinematic control and posture optimization of a redundantly actuated quadruped robot
Bibliographic record
Abstract
Although legged locomotion for robots has been studied for many years, the research of autonomous wheellegged robotics is much more recent. Robots of this type, also described as hybrid, can take advantage of the energy efficiency of wheeled locomotion while adapting to more difficult terrain with legged locomotion when necessary. The Micro Hydraulic Toolkit (MHT), developed by engineers at Defence R&D Canada - Suffield, is a good example of such a robot. Investigation into control and optimization techniques for MHT leads to a better understanding of hybrid vehicle control for terrestrial exploration and reconnaissance. Control of hybrid robots has been studied by several researchers during the last decade. The methodology applied in this work uses an inverse kinematics algorithm developed previously for a hybrid robot Hylos, and implements an optimization technique to minimize torques occurring at crucial actuators. As well, some added functionality is incorporated into the control method to implement stepping maneuvers. This paper will present the results obtained via co-simulation using Matlab's Simulink and a high-fidelity model of MHT in LMS Virtual Lab.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".