Multiple Working Mode Based Door Opening Control and Fault Detection of Mobile Modular and Reconfigurable Robot
Bibliographic record
Abstract
The study in this thesis addresses the problem of opening a door with a modular and reconfigurable robot (MRR) mounted on a wheeled mobile robot platform. The foremost issue with door opening problems is the prevention of occurrence of large internal forces that arise due to position errors or imprecise modeling of the robot or its environment, i.e. the door parameters, specifically. Unlike previous methods that relied on compliance control, making the control design rather complicated, this thesis presents a new concept that utilizes the multiple working modes of the MRR modules. The control design is significantly simplified by switching selected joints of the MRR to work in passive mode during door opening operation. As a result, the occurrence of large internal forces is prevented. Different control schemes are used for control of the joint modules in different working modes. For passive joint modules, a feedforward torque control approach is used to compensate the joint friction to ensure passive motion. For the active joint modules, a distributed control method, based on torque sensing, is used to facilitate the control of joint modules working under this mode. To enable autonomous door opening, an online door parameter estimation algorithm is proposed on the basis of the least squares method; and a path planning algorithm is developed on the basis of Hermite cubic spline functions, with consideration of motion constraints of the mobile MRR. The theory is validated using simulations and experimental results, as presented herein. A distributed fault detection scheme for MRR robots with joint torque sensing is also proposed in this thesis. The proposed scheme relies on filtering the joint torque command and comparing it with a filtered torque estimate that is derived from the nonlinear dynamic model of MRR with joint torque sensing. Common joint actuator faults are considered with fault detection being performed independently for each joint module. The proposed fault detection scheme for each module does not require motion states of any other module, making it an ideal modular approach for fault detection of modular robots. Experimental results have attested the effectiveness of the proposed fault detection scheme.
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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".