IoT-Enhanced Path Planning and Obstacle Avoidance for Autonomous Mobile Robots

Path planning and obstacle avoidance play a key role in how well autonomous mobile robots work and stay safe when they move through tricky areas. Traditional path-planning methods often can’t keep up when new obstacles pop up or existing ones change, which makes them less useful in the real world. To fix these problems, the proposed research work brings in a new IoT-powered path planning and obstacle avoidance system that uses the $\mathrm{D}^{*}$ Lite and Deep Reinforcement Learning (DRL) algorithms. Our approach brings together D* Lite’s quick replanning skills with DRL’s ability to learn and adapt made even better by live data from IoT sensors. We’ve put our model through its paces in different simulated environments set up to be as tricky as real-world navigation. Quantitative analysis shows it’s a big step up from older models. Adding IoT data leads to a 30% boost in how paths are planned and cut the time robots need to get through complex routes by 20%. Plus, using DRL helps robots deal with moving obstacles 25% better, which means fewer potential crashes. This is a big step forward in the field of autonomous robotics. It gives a robust answer that would not just be reactive to the given environmental inputs but also, through proactivity, prompt possible obstacles, further contributing to smoother and safer output. As we move along with the development of our methodology, we foresee broad applications in many sectors enhancing the autonomy and efficiency of robotic systems working in dynamic and unpredictable environments.