Contributions to Contact Modeling and Identification and Optimal Robot Motion Planning
Bibliographic record
Abstract
Industrial robots are widely used as flexible, re-programmable positioning devices in manufacturing plants, and allow to save costs, increase productivity and raise quality. However, they still present capital intensive investments. Therefore, increasing their autonomy and efficiency are topics of ongoing research. With the increasing availability of cheap computation power and low-cost sensors, considerable gains in efficiency and autonomy can be realized through the use of additional sensors and sophisticated data processing and control algorithms. This thesis focuses on two major research topics. The first topic of this thesis deals with contact modeling and identification and aims to advance the autonomy and intelligence of robots that interact with their environment. To this end, robots are equipped with force sensors, in addition to their built-in position sensors, to gather information about their environment. To interpret measurements from these sensors, this thesis formulates and validates contact models, which describe the behavior of robots in contact with their environment. To characterize the interaction of robots with unstructured or uncertain environments, this thesis also develops contact parameter identification algorithms to identify the parameters of contact models. Based on the identified contact parameters, the accuracy and robustness of the low-level control, as well as the task execution can be improved on-line, while knowledge of the contact parameters can be used off-line to design and validate constrained robotic tasks by means of computer simulations. The second topic of this thesis deals with optimal robot motion planning and aims to advance the efficiency of robot motions. By optimizing robot motions, while taking into account their dynamic behavior, robots can fully exploit their capabilities and make full use of their actuators. This thesis develops computer-aided algorithms for planning of robot motions along prescribed geometric paths, called path tracking problems, with time as the main optimality criterion. These path tracking algorithms allow to relieve operators from the burden of manual optimization and tuning, and reduce the downtime of robots, while increasing their productivity.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".