An Analysis Review: Optimal Trajectory for 6-DOF-based Intelligent Controller in Biomedical Application

With technological advancements and the development of robots have begun to be utilized in numerous sectors, including industrial, agricultural, and medical. Optimizing the path planning of robot manipulators is a fundamental aspect of robot research with promising future prospects. The precise robot manipulator tracks can enhance the efficacy of a variety of robot duties, such as workshop operations, crop harvesting, and medical procedures, among others. Trajectory planning for robot manipulators is one of the fundamental robot technologies, and manipulator trajectory accuracy can be enhanced by the design of their controllers. However, the majority of controllers devised up to this point were incapable of effectively resolving the nonlinearity and uncertainty issues of high-degree freedom manipulators in order to overcome these issues and enhance the track performance of high-degree freedom manipulators. Developing practical path-planning algorithms to efficiently complete robot functions in autonomous robotics is critical. In addition, designing a collision-free path in conjunction with the physical limitations of the robot is a very challenging challenge due to the complex environment surrounding the dynamics and kinetics of robots with different degrees of freedom (DoF) and/or multiple arms. The advantages and disadvantages of current robot motion planning methods, incompleteness, scalability, safety, stability, smoothness, accuracy, optimization, and efficiency are examined in this paper

Enhancement Methods for Energy Consumption Prediction in Smart House based on Machine Learning

Energy efficiency in modern homes has recently become a significant issue due to the emergence of smart home infrastructure. Numerous public structures, such as homes, hospitals, schools, and other institutions, use more energy. To come close to meeting the actual energy demand, it is crucial that we create as much energy as we can. Machine learning has various advantages for improving the effectiveness and efficiency of smart home systems and appliances, including managing and lowering energy use. Additionally, as a key component of the smart home idea, we explore the potential integration of machine learning-based on some algorithm methodologies ways to improve power energy consumption system and control. The models were used to identify patterns for smart home and variations in energy consumption. This study's conclusions were used to analyze case studies and forecast energy consumption. Detection Change (of used and generation) for all appliances, which excessive foresees energy use and stops a rise in usage. Predict Future Energy use by using meteorological data and maximizing the supply of energy to forecast future energy generation and use. Finally, using five machine learning algorithms, including the Linear Regression (LR), Gradient Boosting Regression (GBoostR), Decision Tree Regression (DTR), Stochastic Gradient Descent Regression (SGDR), and Bayesian Ridge Regression (BRR), we can measure the Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Absolute Error (RMAE), and Root Mean Squared Percentage Error (RMSPE), in order to determine how well models