<p>The planning of robot motion is crucial for autonomous systems as it allows them to move through complicated spaces correctly and safely. The relevant techniques examined in this study include classical, sampling-based, optimization-based, and even learning-based methods to analyze their pros and cons. These advances are remarkable, but leave many issues that need to be addressed, such as high-dimensional planning, execution planning under real-time conditions, and dealing with uncertainty. The seamless incorporation of Artificial Intelligence and Machine Learning (AIML) creates an avenue to tackle these problems while increasing flexibility, efficiency, and decision making. Exploration of the improvements includes metalearning to tackle generalization of tasks, quantum computing to address high-dimensional computations, and keystone systems that differentiate classical and learning systems. Human ethics and human-robot collaboration are also becoming more and more critical regarding deployment safety and reliability. This review seeks to convey to scientists and engineers the understanding of the current state and existing problems related to the planning of robot motion, as well as possible advancement in this area to further development in this dynamic and vital direction of research. </p>