Modelling and state estimation of exploration rovers
Bibliographic record
Abstract
State estimation is an important element in rover exploration missions.The objective of state estimation is to determine the pose and velocity of the rover by processing measurements from onboard sensors.It usually includes fusing measurement data from different types of sensors.Wheel encoders represent one of the fundamental categories of sensors used for estimation.The so-called classical wheel odometry is widely used in various rover applications.It estimates the rover state by tracking the motions of the two middle wheels in the differential drive based on the related wheel encoders.However, this technique has certain limitations.First, it does not employ redundant measurements.As a consequence, input noise can lead to large uncertainties in the estimated results.Second, it contains a nonlinear estimation model because of the trigonometric functions of the rover orientation.The linearization process that propagates the mean and covariance of the estimated state introduces additional errors.More importantly, it cannot detect the wheel slip and accumulates large estimated errors for rover travelling on soft terrain.The objective of this thesis is to investigate how state estimation can be improved by combining wheel encoder measurements with kinematics, dynamics, and terramechanics modelling.Kinematic and dynamic models are developed in the thesis for a range of rover maneuvres.The interaction between the wheels and soft terrain is also modelled and analyzed employing terramechanics models.A procedure for online soil parameter identification is proposed based on the sensitivity analysis of the terrain traction forces with respect to the variations of the soil parameters.i
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".