Posture reconfiguration and step climbing maneuvers for a wheel-legged robot
Bibliographic record
Abstract
Wheel-legged hybrid robots are known to be extremely capable in negotiating different types of terrain as they combine the efficiency of conventional wheeled platforms and the rough terrain capabilities of legged platforms. The Micro-Hydraulic Toolkit (MHT), developed by Defence Research and Development Canada at the Suffield Research Centre, is one such quadruped hybrid robot. Previously, a velocity-level closed loop inverse kinematics controller had been developed and tested in simulation on a detailed physics-based model of the MHT in LMS Virtual.Lab Motion (VLM). The controller was employed to generate a variety of posture reconfiguration and navigation maneuvers in simulation, such as achieving minimum or maximum chassis height at specific wheel separations, orienting the chassis to a desired pitch angle, or negotiating simulated rough terrain. In this thesis, the aforementioned inverse kinematics controller was improved upon, optimized and adapted to function on the physical MHT vehicle, located in Suffield, Canada. In addition, as a first step towards identifying the deficiencies of the VLM model and, ultimately, validating the model, actuator performance was measured for open loop step and ramp inputs and compared to the simulation results. With the controller implemented on MHT, a subset of the posture reconfiguration and navigation maneuvers previously performed in simulation were tested on the MHT and the robot performance was evaluated. Furthermore, a parametrized algorithm for statically stable step-climbing was developed and successfully verified on the MHT for different step heights.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".