Autonomous robots are becoming increasingly prevalent in various domains, from manufacturing to healthcare and logistics. One of the critical capabilities required for autonomous robots is efficient path planning, enabling them to navigate complex environments while avoiding obstacles. In this project, we explore the application of Hierarchical Reinforcement Learning (HRL) to advance the path planning capabilities of a mobile robot.
Traditional path planning algorithms often face challenges in dealing with complex environments and dynamic obstacles. Hierarchical Reinforcement Learning offers a promising solution by providing a framework that combines the benefits of both high-level and low-level decision-making processes. By breaking down the path planning task into multiple hierarchical levels, the robot can effectively navigate complex environments with improved efficiency and adaptability.
The objective of this project is to develop a mobile robot capable of autonomously planning its path in a given environment using Hierarchical Reinforcement Learning. By leveraging HRL techniques, we aim to create a system that can make high-level decisions for long-term goals while also making low-level decisions to handle immediate obstacles or changes in the environment.
Throughout this project, we will focus on designing and training the hierarchical reinforcement learning architecture, integrating it with the mobile robot's perception and control systems, and evaluating its performance in simulated or real-world scenarios. We will explore concepts such as state representation, action selection, reward design, and training methodologies specific to hierarchical reinforcement learning.
By successfully implementing this project, we not only advance our understanding of reinforcement learning techniques but also enhance the autonomous navigation capabilities of mobile robots. This project opens up possibilities for deploying robots in dynamic environments, such as warehouses, hospitals, or even autonomous vehicles, where efficient path planning is crucial for safe and efficient operation.