Deep Reinforcement Learning with PPO for Autonomous Mobile Robot Navigation Using ROS 2 Framework
Rana A · International Journal for Research in Applied Science and Engineering Technology · 2025
This paper brings robot learning to life by showing how a humble TurtleBot3 can teach itself to navigate using an approach inspired by how humans learn through trial and error. We've created a custom training playground where the robot learns from its 360-degree laser "vision" (like constantly feeling its surroundings with outstretched arms) to smoothly move through spaces without collisions. We have established a link between virtual practice sessions and actual performance by integrating the robot's operating system (ROS 2) with sophisticated AI training tools (PPO algorithm). Our learner achieved an 82% success rate in navigating unfamiliar spaces after countless simulated trial runs, which are the robotic equivalent of a student taking practice exams. The main finding is intriguing: with the correct training framework, robots can acquire surprisingly human-like navigation skills. This is true even though the robot performs marginally better in simulation than in messy reality, where unexpected lighting and textures can confuse its sensors. This work is unique because we have kept things realistic by concentrating on solutions that can be implemented in homes or workplaces and utilizing reasonably priced hardware. Although the system isn't flawless—it occasionally pauses in confined spaces like a cautious driver—it shows how artificial intelligence can enable machines to move more naturally.