Visual Servoing of a 5-DOF Mobile Manipulator Using a Panoramic Vision System
Bibliographic record
Abstract
In this paper, a novel visual servoing technique for a S-DOF mobile manipulator with an eye-to-hand camera configuration is introduced. The proposed technique can be categorized as an image based (or 2D) visual servoing using a fixed camera in conjunction with a conic mirror (aka, an omni-directional camera system) providing panoramic vision. Two fictitious landmarks mounted on robot's end-effector along with their mirror reflections, when viewed by the camera, provide enough information for 3D reasoning based on the four points viewed on the image plane. Instead of directly using the image features associated with these four points, five new image features are chosen to make the image Jacobian of full rank. A dual estimation/control strategy based on Extended Kalman Filter (EKF) is utilized to (I) estimate camera's intrinsic and extrinsic parameters, and (2) track the coordinates of the landmarks and their reflections on the image plane. The relationship between the translational and rotational velocity of a frame attached to the robot's end-effector and rate of change of the proposed image features are fully formulated The robustness of the proposed visual servoing technique is illustrated through computer simulations.
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.
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) | 0.002 | 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".