Computer
Noor Abdul Khaleq Zghair; Ahmed S. Al-Araji
Abstract
A mobile robot's major purpose is to get to its destination by traveling over an optimum path defined by various parameters such as time, distance, and the robot's safety from any impediments in its path. As a result, the backbone of the autonomous mobile robot is path planning and obstacle avoidance. ...
Read More ...
A mobile robot's major purpose is to get to its destination by traveling over an optimum path defined by various parameters such as time, distance, and the robot's safety from any impediments in its path. As a result, the backbone of the autonomous mobile robot is path planning and obstacle avoidance. Several algorithms for path planning and obstacle avoidance have been presented by various researchers, each with its own set of benefits and drawbacks. This paper focuses on two parts; the first part finds the short and smooth collision-free path for a mobile robot to navigate in a static environment based on two proposed hybrid algorithms. The first hybrid is between Firefly Algorithm (FA) and Modify Chaotic Particle Swarm Optimization (MCPSO), namely (HFACPSO), while the other hybrid is between Genetic Algorithm (GA) and MCPSO, namely (HGACPSO).The second part suggests an algorithm planner for improving the efficiency of the route-planning algorithm with moving obstacle avoidance by adjusting the velocity or re-planning the path for the mobile robot. To demonstrate the effectiveness of the proposed algorithms in terms of the shortest path length and collision-free, as well as obtaining optimal or near-optimal wheel velocities with the minimum number of iterations. The proposed hybrid (FAMCPSO) algorithm provides enhancement on the path length equal to (0.82%) compared to the firefly algorithm (FA). Moreover, the hybrid (GAMCPSO) algorithm enhancement on the path length equals (0.67%) compared to the genetic algorithm (GA). All methods are simulated in a static and dynamic obstacle environment using MATLAB 2018b.
Communication
Zaid Hashim Jaber; Dheyaa Jasim Kadhim; Ahmed Sabah Al-Araji
Abstract
Massive Multiple-Input Multiple-Output (MIMO) is an extension of the conventional MIMO in the wireless systems which improves both of the access density and the spectral efficiency by adding a massive number of antenna array at the base station (BS). Massive MIMO increases the spectral efficiency by ...
Read More ...
Massive Multiple-Input Multiple-Output (MIMO) is an extension of the conventional MIMO in the wireless systems which improves both of the access density and the spectral efficiency by adding a massive number of antenna array at the base station (BS). Massive MIMO increases the spectral efficiency by using the beamforming. Besides, the beamforming in massive MIMO improves the energy efficiency by focusing the energy in the desired direction instead of the omnidirectional propagation. In this paper, we propose and discuss different beamforming objectives in both the uplink and the downlink channels. These proposed objectives can be either use the beamforming of the desired signal without nulling the interference or use the beamforming with interference nulling. The beamforming with nulling objectives have better performance than those without nulling but this leads to a higher computational complexity as well. The results of this paper show and compare the performance of these objective including the spectral efficiency and energy efficiency as well as the computational complexity.
Control
Nasir Ahmed Alawad; Amjad J Humaidi; Ahmed Sabah Alarejee
Abstract
Exoskeleton robots help users with mechanical forces by recognizing their intentions, and they require a lot of energy efficiency, a lot of load capacity, and a good fit. A basic one degree of freedom (DOF) construction was devised in this work, which was mostly used in the knees of exoskeleton robots. ...
Read More ...
Exoskeleton robots help users with mechanical forces by recognizing their intentions, and they require a lot of energy efficiency, a lot of load capacity, and a good fit. A basic one degree of freedom (DOF) construction was devised in this work, which was mostly used in the knees of exoskeleton robots. The exoskeleton is a small robotic device used for knee injury training. It is a nonlinear mathematical model with many mechanical factors that might vary and produce uncertainty, as well as external disturbances that can be utilized to monitor control. The transitioning process is frequently organized using tracking differentiator TD to resolve the conflict between system speed and overshoot. An active disturbance rejection control (ADRC) with a modified tracking differentiator is described to tackle these challenges, enhance control accuracy, and reduce settling time for exoskeleton modified trajectory differentiator (MTD).Simulation tests showed that (MTD) reduced the tracking error by 36%, when compared with the improved TD1 and 37.5% for Hans TD2 at uncertainty case . Despite the presence of several model uncertainties, the suggested training knee exoskeleton robot system using the MTD-ADRC was able to achieve the necessary target value. Control design and analysis can be done with Matlab and Simulink
