Fault-Tolerant Cooperative Control Design of Multiple Wheeled Mobile Robots
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Bibliographic record
Abstract
This brief investigates fault-tolerant cooperative control (FTCC) strategies for multiple differentially driven autonomous wheeled mobile robots (WMRs) in the presence of actuator faults during formation operation. First, for normal/fault-free cases and for preparation to the faults occurrence cases, an integrated approach combining input-output feedback linearization and distributed linear model predictive control techniques is designed and implemented on a team of WMRs to accomplish a formation task. Second, when actuator faults occur in one of the robots of the team, two cases are explicitly considered: 1) if the faulty robot cannot complete its assigned task due to a severe fault, then the faulty robot has to get out from the formation mission, and an FTCC strategy is designed such that the tasks of the WMRs team are reassigned to the remaining healthy robots to complete the mission with graceful performance degradation and 2) if the faulty robot can continue the mission with degraded performance, then the other team members reconfigure their controllers considering the capability of the faulty robot. Thus, the FTCC strategy is designed to re-coordinate the motion of each robot in the team. Within the proposed scheme, a fault detection and diagnosis unit using a two-stage Kalman filter to detect and diagnose actuator faults is presented. Then, the FTCC problem is formulated as an optimal assignment problem, where a Hungarian algorithm is applied. Moreover, a collision avoidance algorithm based on mechanical impedance principle is proposed to avoid the potential collision between the healthy robots and the faulty ones. Formation operation of the robot team is based on a leader-follower approach, while the control algorithm is implemented in a distributed manner. The results of real experiments demonstrate the effectiveness of the proposed FTCC scheme in different fault scenarios.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it