Hazim Albedran

This work presents a generalized framework for solving the inverse kinematics problem of robotic manipulators by introducing two approaches: optimization-based and learning-based approaches within a unified architecture. The optimization-based inverse kinematic solver is formulated as a minimization problem of the error in localization of the end-effector. On the other hand, the artificial neural network’s inverse kinematic solver is presented as a subsequent operation to a given straightforward forward kinematics analysis. The two proposed formulations are applicable to robot manipulators with any synthesis and degrees of freedom. To validate the proposed solvers, a low-cost 6-DOF robotic platform was developed, including a host-embedded system that enables real-time interaction between a virtual environment and a physical manipulator. Different optimization algorithms and different learning algorithms were used and their effect was compared to evaluate the efficiency of the proposed framework. The two approaches were analyzed and compared with respect to accuracy, computational cost, and real-time suitability. Results show that optimization methods provide higher precision and require longer computation time, whereas the artificial neural network-based method achieves significantly faster responses with acceptable approximation error. Experimental validation demonstrates the robustness and practical applicability of the framework, which is recommended for high-degree-of-freedom manipulators where analytical and closed-form solutions do not exist.

Drones, specifically quadcopters, have increased in importance during the last years due to their wide range of applications, from civil applications to military employment. One of the most important issues in quadcopters is the efficient control system. While many researchers have dealt with building control systems for symmetric quadcopters, this work presents an efficient control system for asymmetric quadcopters using evolutionary computations. The problem is well-defined throughout the paper, and the methodology is explained in detail in the respective sections. A genetic algorithm is used to tune the weighting matrix of the control system after formulating the control system as an optimization problem. The genetic algorithm was fast and active to increase the performance of the proposed system.

As a result of rainfall in large quantities, the leachate generated under the municipal solid waste (MSW) is increased, which leaks to the groundwater aquifers and pollutes it. Accurate evaluation of leachate leaks levels has long been regarded as a problem in Iraq due to a lack of reliable data and costly measuring costs. This work proposes a novel fuzzy expert system to predict the pollution status of the underground water in sandy soils. The expert system consists of two subsystems; fuzzy logic system and crisp logic system. The expert system is trained using a data set developed by finite element analysis of sandy soil subjected to contamination materials.