Bioinspired optimization on controlled anthropomorphic manipulator robots
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Bioinspired optimization algorithms, derived from biological processes such as bacterial foraging and swarm behavior, have shown increasing potential in addressing high-dimensional, nonlinear, and time-varying problems in engineering. Their integration into robotic control architectures enables the development of adaptive, model-flexible schemes that are robust to uncertainty and real-time constraints. Anthropomorphic manipulator robots, widely used in manufacturing and medical applications, require high-performance motion control under structural uncertainty, dynamic perturbations, and limited sensing. This paper proposes a unified and robust control scheme that integrates three key components: (i) a bacterial foraging optimization algorithm for offline initialization of controller weights, (ii) B-spline artificial neural networks for online adjustment of adaptive control gains, and (iii) a robust motion control law based on integral reconstruction theory, which eliminates the need for velocity measurement or full dynamic models and avoids high-gain compensation. This architecture overcomes several limitations of classical model-based, PID, or adaptive-only approaches by combining learning, compensation, and optimization within a scalable framework. The proposed method is validated through multiple simulation studies involving anthropomorphic manipulators with documented physical parameters and subjected to varying disturbances. Comparative analysis demonstrates superior tracking precision, reduced control effort, and faster convergence dynamics. These results confirm the practical viability of the proposed framework for motion control in dynamically uncertain robotic platforms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 it