Jumping behaviour for a wheeled quadruped robot: simulation and experiments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, we describe a new jumping behaviour developed for the quadruped robot, PAW (Platform for Ambulating Wheels). The robot has very few degrees of freedom and no knee joints. It employs springy legs and wheels at the distal ends of the legs to achieve various modes of legged, wheeled, and hybrid locomotion, such as high-speed breaking, bounding, and presently jumping. The jumping maneuver developed in this manuscript is designed specifically to take advantage of the wheels on the robot and compliance in its legs and it involves the following principal stages: acceleration to jumping speed, body positioning via front hip thrusting, rear leg compression and thrusting, and flight. A high-fidelity MSC.ADAMS/Simulink co-simulation was developed and used to test and optimize the jumping process. Because of the strong coupling between the parameters defining the jump maneuver, manual parameter tuning is difficult and thus a genetic algorithm is employed for the optimization process. The data generated by the genetic algorithm is further used for the fitting of a quadratic response surface, which allows identifying those parameters that contribute most to a successful jump. Finally, the jumping maneuver is implemented on the physical PAW to demonstrate its feasibility on a hybrid quadruped, and to provide insights into the robot response during this highly dynamic maneuver.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.000 | 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 it