Impedance control without force sensors with application in homecare robotics
Bibliographic record
Abstract
This thesis addresses the problem of interaction control between robot manipulator and the manipulated object in a homecare project. This project aims to use homecare robots at the elderly or disabled people’s home to provide necessary aid and assistance. The robot manipulator is to be operated in autonomous mode or teleoperation mode. The possible first aid or assistance requires direct interaction between the remote side robot manipulator and the human body. To guarantee the compliant interaction between the manipulator and the human body, impedance control was applied. In impedance control, neither the force nor the actual motion of the manipulator is controlled. The dynamic relationship between the interaction force and the resulting motion is controlled so that the interaction force will be monitored and kept at an acceptable range. To shape the mechanical impedance to any desired value as we wish, the remote side interaction force sensing is required. The interaction force could be sensed by a force sensor. Force sensors have a lot of inherent limitations such as narrow bandwidth, sensing noise, and high cost. To avoid a force sensor due to its limitations, sliding mode observers will be applied to estimate the interaction force. The estimated interaction force will be used in the impedance control algorithms. The observer and controller framework will be formulated and the solvability will be discussed thoroughly. In addition, the proposed approach will be compared with some available approaches to show its advantages over others. Bilateral impedance control will be applied in a teleoperation system. The master side impedance controller is to ensure the robust stability of the teleoperation system. The remote slave side impedance controller is used so that the interaction force will be monitored and kept at some acceptable range. Desired impedance parameters selection will be discussed considering the compromise between robust stability and performance. Also, in order to deal with the uncertainties in operator and environment dynamics, a robust performance guaranteed controller synthesis approach will be proposed. Gain-scheduling control could guarantee the stability and the robust performance under those uncertainties.
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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 | 0.000 |
| 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 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".