Redundancy Resolution for Singularity Avoidance of Wheeled Mobile Manipulators
Bibliographic record
Abstract
Mobile robots consist of a mobile platform with manipulator can provide interesting functionalities in a number of applications, since, combination of platform and manipulator causes robot operates in extended work space. The analysis of these systems includes kinematics redundancy that makes more complicated problem. However, it gives more feasibility to robotic systems because of the existence of multiple solutions in a specified workspace. This paper presents a novel combination of evolutionary algorithms and artificial potential field theory for motion planning of mobile manipulator which guaranteed collision and singularity avoidance. In the proposed algorithm, the developed concepts of potential field method are applied to obstacle avoidance and interaction of mobile base with manipulator is used as a new idea for singularity avoidance ability of intermediate links for mobile operations. For this purpose, kinematic and dynamic modeling is derived to define redundant solutions. Afterward, methods from potential field theory combine with evolutionary algorithms to provide an optimum solution among possibly of redundancy resolution scheme. A controller based on dynamic feedback linearization is augmented to track the selective motion trajectory. Simulation results verify obstacle avoidance, singularity avoidance for the manipulators and asymptotic convergence of the robots errors.
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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 | 0.000 | 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) | 0.001 | 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 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".