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In the era of industrial revolution 4.0, machines are not only used for computation but also intelligent enough to take decision and controls system’s behavior. Machines are made to learn and behave like an intelligent human known as “Artificially Intelligent System”. By imposing powerful sensors to machines it can detect abnormal situation and holds decision making power. Large data storage gives memorizing ability to machines so that machines are able to decide how to behave in particular situation.

In the proposed paper, we have discussed about how motion planning integrated to AI. Motion planning is computational problem to decide path from source to destination without collision with any other object. Motion planning based on Artificial intelligence gives optimal strategy. When machine is in new state it needs to configure map and calculate estimated cost of moving one place to another place where AI plays key role, AI gives ability to think and decide. Here we Integrate Probabilistic Roadmap algorithm to K Nearest Neighbors algorithm of AI which gives variant of this two algorithm. KNN works on classification of data set and training phase is short. This variant is able to separate whole map data into different section so that machine can easily determine in which part destination lies. The proposed paper thoroughly describes new variant and challenges in this area.