Modeling and Dynamic Control of Autonomous Ground Mobile Manipulators
Bibliographic record
Abstract
A Mobile Manipulators (MMs) in particular is an articulated robotic arm mounted on a (space, ground, aerial, surface or underwater) mobile platform. The mobile platform increases the size of the manipulator’s workspace and as a result the increased degree of mobility enables better positioning of the manipulator in different configurations for efficient task execution. However, it is a challenge to effectively control such systems outside the lab and engineered environments such as those encountered in rough outdoor environments and urban search and rescue applications where numerous uncertainties exist. In these environments numerous model approximations have been used which cannot be considered when working in real world outdoor conditions. The main focus of this work is to develop needed mechanisms to enable the deployment of MMs in unstructured terrains. For this a kinematic and dynamic model for a generalized mobile manipulator without using approximations is developed. In addition, such improved model is used for the control and robustness of mobile manipulator controllers in the existence of dynamic uncertainties which has not been extensively considered in prior work. Two new control algorithms are developed and shown to solve the problem at hand with much better accuracy when compared to prior proposed solutions. As a result of the proposed developed methodologies the proposed control system architecture is shown to be applicable for the control of MMs performing a cooperative task with other robots or humans. The control mechanism enables the MMs to execute complex tasks when it is subject to dynamic uncertainties resulted from cooperation with humans or other autonomous robots when working in unknown, dynamic, heterogeneous outdoor rough terrains/environments. The approach is shown to be effective with the use of two different control solutions: i) robust sliding mode backstepping kinematic into dynamics, and ii) stable robust adaptive Sliding mode backstepping CMAC Neural Network control where both control systems use a Lyapunov function stability. Simulation tests using a detailed SIMMECHANICS /SIMULINK model of the employed MMs are presented to illustrate and demonstrate the performance of the developed control mechanisms.
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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 | 0.001 | 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) | 0.002 | 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".