Development and performance comparison of extended Kalman filter and particle filter for self-reconfigurable mobile robots
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 develop two filters, extended Kalman filter (EKF) and particle filter (PF), for autonomous docking of mobile robots and compare the performances of the two filers in terms of accuracy. Robots are equipped with IR emitters/receivers and encoders, and their data is used to estimate the distance and orientation of robots, which is needed for docking. The two state estimation methods are compared in simulations under different conditions. Simulation results demonstrate that the estimation accuracy of the EKF is higher than PF when the initial state is correctly estimated. However, when the initial state is not estimated correctly, the state estimation of EKF does not converge to the true value. On the other hand, PF state estimation successfully converges to the true value and the error is more consistent. The result of this work can help researchers and practitioners to design and use proper filters for docking applications.
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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