Robotics architectures based machine learning and deep learning approaches

Abstract

Robotics has been playing a vital role in our daily lives with a wide range of applications to improve the quality of life. With a variety of usable applications in the medical, manufacturing, and transportation industries, there is a continuous need for improving the performance of robotics for the importance of precision in executing commands and tasks. The implementation of precise commands has led to intense research on approaches to improve the performance of robotics. Machine Learning (ML) and Deep Learning (DL) have been drawing attention to applying architectures and algorithms to robotics which imposed a positive impact on the field of robotics. ML and DL applications in robotics include areas of computer vision, imitation learning, self-supervised learning, assistive and medical technologies, multi-agent learning, and manufacturing. This paper provides a comprehensive review of autonomous vs automatic robotics, robotic applications, extreme learning machine methods, and ML for soft robotics applications, in addition, to discussing the challenges, and future trends for AI applications in robotics applications.

Energy Efficient Path Planning Scheme for Unmanned Aerial Vehicle Using Hybrid Generic Algorithm-Based Q-Learning Optimization

“…Update the Qvalues based on the Q-learning equation using the hybrid technique afterward. It enables to expression of the state of the UAV using pertinent variables such as location, velocity, and distance from obstacles and balances the exploration of new actions with the exploitation of obtained knowledge using an exploration-exploitation trade-off parameter [18]. The Q-values based on the hybrid GA-Qlearning are updated according to the following equation.…”