Coverage path planning (CPP) is one of the current researches for mobile robots. This study presents a new approach for solving CPP. In this approach the coverage area is divided into small squares where the squares diagonal is the size of the robot tool. Four fundamental motions in a square are defined, which are straight, left turn, right turn and U-turn. A cost function is taken in which a fixed cost for each fundamental motion in a square is used and costs for all moves are summed up to get an approximated cost for a path. This function makes it possible to find the better path between two paths with equal repetitive visits. Furthermore genetic algorithm (GA) is used to find the best path to cover an area. Via crossover, selection and mutation GA improves current paths which leads to optimal, near optimal solutions. Simulation results are taken with reasonable areas.

The use of renewable energy, especially wind power, is the most practical way to mitigate the environmental effects that various countries around the world are suffering from. To meet the growing need for electricity, wind energy is, nevertheless, being used more and more. Researchers have come to understand that a near-perfect output power estimate must be sacrificed. Variations in the weather influence wind energy, including wind speed, surface temperature, and pressure. In this study, the wind turbine output power was estimated using three approaches of artificial neural networks (ANNs). The multilayer feed-forward neural network (MLFFNN), cascaded forward neural network (CFNN), and recurrent neural network (RNN) were employed for estimating the entire output power of wind turbine farms in Egypt. Therefore, each built NN made use of wind speed, surface temperature, and pressure as inputs, while the wind turbine’s output power served as its output. The data of 62 days were gathered from wind turbine farm for the training and efficiency examination techniques of every implemented ANN. The first 50 days’ worth of data were utilized to train the three created NNs, and the last 12 days’ worth of data were employed to assess the efficiency and generalization capacity of the trained NNs. The outcomes showed that the trained NNs were operating successfully and effectively estimated power. When analyzed alongside the other NNs, the RNN produced the best main square error (MSE) of 0.00012638, while the CFNN had the worst MSE of 0.00050805. A comparison between the other relevant research studies and our suggested approach was created. This comparison led us to the conclusion that the recommended method was simpler and had a lower MSE than the others. Additionally, the generalization ability was assessed and validated using the approved methodology.

Hexapod robots are widely used in applications such as search and rescue, exploration, and surveillance due to their improved mobility and stability on uneven terrain. However, conventional hexapod robots typically suffer from issues such as heavy weight and complex design, which limits their effectiveness in certain scenarios. In this paper, we propose a new hexapod robot that addresses the issues of weight and complexity often associated with conventional hexapod robots. Our proposed design uses telescopic legs connected to pivot joints at the hips, resulting in a two-DOFs-leg configuration. The robot’s six legs are aligned with a parallelepiped body, with each leg configured with a revolute joint and a prismatic joint. With a total of twelve degrees of freedom (DOFs), the proposed robot can move in a straight line or turn. Our solution for stabilizing the robot’s horizontally oriented body on uneven terrain, using analytical kinematics, inverse kinematics, and geometric analysis, is a valuable contribution. Additionally, we present simulation of path generation and gait design for various inclined planes and stairs using a virtual prototype modeled in MATLAB/Simulink Simscape Multibody. The results of our simulated and physical prototype experiments demonstrate that the proposed design and body control algorithm effectively maintain the orientation of the robot’s body usually stable while it moves on different types of surfaces. Overall, our proposed hexapod robot using telescopic legs offers a promising solution for achieving improved mobility and stability on uneven terrain, with reduced weight and complexity compared to conventional hexapod robots.

Mobile robots are widely used in many applications, such as transportation, military domain, searching, guidance, rescue, hazard detection, and carpet cleaning. Because mobile robots usually carry limited power sources, such as batteries, energy saving is an important concern. In particular, differential wheeled mobile robots that have two independently movable wheels are popular because of relatively low mechanical complexity to achieve three-dimensional motion on a ground. This article presents a new wheeled device with a redundant drive system which consists of three independently movable actuators and two planetary gears to connect the actuators to two wheels. The proposed system is able to continue its motion safely when one of the motors breaks down for some causes and also able to operate over a longer period of time due to energy saving consumption property. Experimental results show that the proposed method is effective in saving energy approximately by 20.45% for linear motion and 13.05% for circular motion compared to a conventional one.

This paper presents the design of a four omni-wheeled mobile robot consisting of four omni wheels, with each wheel connecting to a separate DC motor. Additionally, the presence of a telescopic leg with a linear RC servo actuator enables the robot to adapt to various landscape changes, including obstacle overcoming. We have designed and manufactured the physical prototype of the robot based on the simulation results. The proposed robot can traverse in both vertical and horizontal directions without altering its orientation, thereby enhancing its stability during operation. The experimental results confirm the robot’s effectiveness in autonomously adapting its position in response to sudden changes in the landscape, enabling it to navigate and climb steps successfully.