Advanced Topics in Nonlinear Programming and Its Application in Robotic Control

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: closed (20 December 2024) | Viewed by 2427

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Guest Editor
Department of Land Surveying and Geo-Informatics (LSGI), The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
Interests: robotics; control systems; SLAM; autonomous navigation

Special Issue Information

Dear Colleagues,

Optimization algorithms play a crucial role in any practical, real-world system. Since the goal of an optimization algorithm is to find an optimal solution for a given objective function, this goal becomes extremely difficult in the case of a nonlinear objective function. Unlike linear optimization, where the solution space is typically convex and solutions can be found using straightforward algebraic techniques, nonlinear optimization often presents challenges due to multiple local optima, non-convexities, and complex constraints. However, most real-world systems are nonlinear, making it challenging to accurately model and control them using linear techniques. Consider the example of robotic systems, which are a combination of several mechanical and electrical components. Each mechanical or electrical component introduces a degree of nonlinearity, which is compounded because the components are interconnected in a complex configuration of serial and parallel connections.

Several nonlinear algorithms have been proposed in the literature to address the problem related to the modeling and control of robotic systems, e.g., Newton’s and quasi-newton’s methods, interior point, trust region, sequential quadratic programming, stochastic optimization, augmented Lagrangian, and penalty function are a few examples of such algorithms. Several derivative-free algorithms have also been introduced in the literature, such as evolutionary and genetic algorithms, particle swarm search (PSO), simulated annealing, etc. These algorithms have been applied to solve a wide variety of problems related to robots, e.g., kinematic and dynamic modeling, formulating control laws, task planning, path planning, sensor fusion, state estimation, etc., are just a few examples of problems in robotics solved using nonlinear optimization algorithms. Optimization algorithms have given way to several paradigms of control theory, e.g., optimal control, adaptive control, stochastic control, and impedance control. Needless to say, optimization algorithms are an integral component of robotic systems.

We are organizing this Special Issue to gather the latest research on nonlinear optimization and its applications in robotic modeling and control systems. It is out hope that the papers submitted in this SI will help to advance the research in the field of optimization and control.

Dr. Ameer Hamza Khan
Guest Editor

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Keywords

  • nonlinear optimization
  • robotic control
  • optimal control

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Published Papers (2 papers)

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Research

26 pages, 12562 KiB  
Article
A Study on the Performance of Adaptive Neural Networks for Haze Reduction with a Focus on Precision
by Ahmed Alshahir, Khaled Kaaniche, Ghulam Abbas, Paolo Mercorelli, Mohammed Albekairi and Meshari D. Alanazi
Mathematics 2024, 12(16), 2526; https://doi.org/10.3390/math12162526 - 15 Aug 2024
Cited by 1 | Viewed by 806
Abstract
Visual clarity is significantly compromised, and the efficacy of numerous computer vision tasks is impeded by the widespread presence of haze in images. Innovative approaches to accurately minimize haze while keeping image features are needed to address this difficulty. The difficulties of current [...] Read more.
Visual clarity is significantly compromised, and the efficacy of numerous computer vision tasks is impeded by the widespread presence of haze in images. Innovative approaches to accurately minimize haze while keeping image features are needed to address this difficulty. The difficulties of current methods and the need to create better ones are brought to light in this investigation of the haze removal problem. The main goal is to provide a region-specific haze reduction approach by utilizing an Adaptive Neural Training Net (ANTN). The suggested technique uses adaptive training procedures with external haze images, pixel-segregated images, and haze-reduced images. Iteratively comparing spectral differences in hazy and non-hazy areas improves accuracy and decreases haze reduction errors. This study shows that the recommended strategy significantly improves upon the existing training ratio, region differentiation, and precision methods. The results demonstrate that the proposed method is effective, with a 9.83% drop in mistake rate and a 14.55% drop in differentiating time. This study’s findings highlight the value of adaptable neural networks for haze reduction without losing image quality. The research concludes with a positive outlook on the future of haze reduction methods, which should lead to better visual clarity and overall performance across a wide range of computer vision applications. Full article
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21 pages, 9368 KiB  
Article
Advanced Neural Classifier-Based Effective Human Assistance Robots Using Comparable Interactive Input Assessment Technique
by Mohammed Albekairi, Khaled Kaaniche, Ghulam Abbas, Paolo Mercorelli, Meshari D. Alanazi and Ahmad Almadhor
Mathematics 2024, 12(16), 2500; https://doi.org/10.3390/math12162500 - 13 Aug 2024
Cited by 2 | Viewed by 1098
Abstract
The role of robotic systems in human assistance is inevitable with the bots that assist with interactive and voice commands. For cooperative and precise assistance, the understandability of these bots needs better input analysis. This article introduces a Comparable Input Assessment Technique (CIAT) [...] Read more.
The role of robotic systems in human assistance is inevitable with the bots that assist with interactive and voice commands. For cooperative and precise assistance, the understandability of these bots needs better input analysis. This article introduces a Comparable Input Assessment Technique (CIAT) to improve the bot system’s understandability. This research introduces a novel approach for HRI that uses optimized algorithms for input detection, analysis, and response generation in conjunction with advanced neural classifiers. This approach employs deep learning models to enhance the accuracy of input identification and processing efficiency, in contrast to previous approaches that often depended on conventional detection techniques and basic analytical methods. Regardless of the input type, this technique defines cooperative control for assistance from previous histories. The inputs are cooperatively validated for the instruction responses for human assistance through defined classifications. For this purpose, a neural classifier is used; the maximum possibilities for assistance using self-detected instructions are recommended for the user. The neural classifier is divided into two categories according to its maximum comparable limits: precise instruction and least assessment inputs. For this purpose, the robot system is trained using previous histories and new assistance activities. The learning process performs comparable validations between detected and unrecognizable inputs with a classification that reduces understandability errors. Therefore, the proposed technique was found to reduce response time by 6.81%, improve input detection by 8.73%, and provide assistance by 12.23% under varying inputs. Full article
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