Bibliographic record
Abstract
Modular and reconfigurable robot has been one of the main areas of robotics research in recent years due to its wide range of applications, especially in aerospace sector. Dynamic control of manipulators can be performed using joint torque sensing with little information of the link dynamics. From the modular robot perspective, this advantage offered by the torque sensor can be taken to enhance the modularity of the control system. Known modular robots though boast novel and diverse mechanical design on joint modules in one way or another, they still require the whole robot dynamic model for motion control, and modularity offered in the mechanical side does not offer any advantage in the control design. In this work, a modular distributed control technique is formulated for modular and reconfigurable robots that can instantly adapt to robot reconfigurations. Under this control methodology, a modular and reconfigurable robot is stabilized joint by joint, and modules can be added or removed without the need of re-tuning the controller. Model uncertainties associated with load and links are compensated by the use of joint torque sensors. Other model uncertainties at each joint module are compensated by a decomposition based robust controller for each module. The proposed distributed control technique offers a ‘modular’ approach, featuring a unique joint-by-joint control synthesis of the joint modules. Fault tolerance and fault detection are formulated as a decentralized control problem for modular and reconfigurable robots in this thesis work. The modularity of the system is exploited to derive a strategy dependent only on a single joint module, while eliminating the need for the motion states of other joint modules. While the traditional fault tolerant and detection schemes are suitable for robots with the whole dynamic model, this proposed technique is ideal for modular and reconfigurable robots because of its modular nature. The proposed methods have been investigated with simulations and experimentally tested using a 3-DOF modular and reconfigurable robot.
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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) | 0.001 | 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".