Manipulator transition to and from contact tasks: a discontinuous control approach
Bibliographic record
Abstract
The stability and control of robotic manipulators during the execution of tasks that require the manipulator to make a transition from noncontact motion to contact motion, or vice versa, are investigated. A dynamic model of the manipulator during noncontact and contact motion is developed. This model includes the effect of the inevitable collision that occurs between the manipulator end effector and the work environment during the transition from noncontact to contact motion. The work environment that the manipulator comes into contact with is modeled as a very still surface. The dynamic model of the robot during this transition is transformed through a nonlinear coordinate transformation into a new set of generalized coordinates in which the form of the dynamics is greatly simplified. A discontinuous control is proposed for the robotic manipulator system. It is shown that with this discontinuous control applied to the system, the closed-loop system can be treated as a generalized dynamical system. Using the theory associated with generalized dynamical systems, it is possible to extend Lyapunov stability analysis to systems with discontinuous controls. The system dynamics is written as a contingent equation to which a set valued control function is applied. Within this mathematical framework, the uniform asymptotic stability in the larger of the closed-loop systems is proved. The controller has several desirable properties, including the ability to return to contact motion if the manipulator end effector inadvertently leaves the surface due to some external disturbance acting on the system.>
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".