Machine Learning As a Part of Artificial Language in Decision Science: A Literature Review: Exploring the Role of Machine Learning in Dynamic Motion Planning for Mobile Robots

Abstract

Motion planning in dynamic or in an uncertain environment is an important problem in the field of mobile robots that is used in real world applications. In motion planning motion behaviors of the mobile robot can be classified into two fundamental behaviors obstacle-avoidance and goal-seeking. Robots that operate in the real world need to respond rapidly to changes in the environment.A plan with available data to the robot’s goal quickly becomes invalidated as and when the environment changes or the robot receives new information. Then the challenge in mobile robots is replanning the paths as quickly as possible. Especially challenging environments are dynamic obstacles, such as personal space around people, buffer zones around dangerous vehicles, and rough terrain. Because sensors are imperfect, robots navigating in real time dynamic environments must re-plan whenever they receive new sensory data in order to ensure a safe, low-cost path.Learning is acquiring new or modifying existing knowledge, behaviors, skills, values, or preferences and may involve synthesizing different types of information. The ability to learn is possessed by humans, animals and some machines. Human learning may occur as part of education, personal development, or training. It may be goal-oriented and may be aided by motivation. Learning may occur with or without conscious.