Path Planning of Mobile Robot Using Reinforcement Learning

In complex planning and control operations and tasks like manipulating objects, assisting experts in various fields, navigating outdoor environments, and exploring uncharted territory, modern robots are designed to complement or completely replace humans. Even for those skilled in robot programming, designing a control schema for such robots to carry out these tasks is typically a challenging process that necessitates starting from scratch with a new and distinct controller for each task. The designer must consider the wide range of circumstances the robot might encounter. This kind of manual programming is typically expensive and time consuming. It would be more beneficial if a robot could learn the task on its own rather than having to be preprogrammed to perform all these tasks. In this paper, a method for the path planning of a robot in a known environment is implemented using Q-Learning by finding an optimal path from a specified starting and ending point.