Robust Adaptive Position and Force Tracking Control Strategy for Door-Opening Behaviour
Bibliographic record
Abstract
The door-opening task is a key step for the indoor rescue and monitoring of a mobile manipulator.However, the contact effect between the gripper and the door handle may produce excessive internal forces to damage mechanical devices because of position errors or the imprecise modelling of the robot and operation environment.To successfully suppress the excessive internal forces and assure the proper posture of the mobile manipulator under holonomic and non-holonomic constraints, a robust adaptive position/force control algorithm was proposed to track the desired posture and force in opening a door to avoid the complexity of compliant mechanism and the unpredictability of the contact stiffness in traditional impedance control.Dynamic simulation studies with MATLAB and RecurDyn were used to verify the dynamic model of the system and obtain the expected positions and forces during door opening.Simulation results and experiments show that the proposed method is robust in modelling errors, joint frictions and environment disturbances and meets the requirement for opening a door with a handle and suppressing excessive internal forces.This study offers reference data and the control method for future real-world door-opening operation in different environments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".