Deep Reinforcement Learning Autonomous Warehouse AGV Navigation
Proximal Policy Optimization (PPO) agent trained in PyBullet to navigate dynamic factory obstacles without hand-crafted heuristics.
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Project Technical Overview
Trains an automated guided vehicle (AGV) in a continuous 3D physics environment. The agent takes 2D simulated LiDAR range findings as input and outputs continuous steering angles and throttle, learning collision-free shortest-path navigation in dense pedestrian warehouses.
Read the foundational IEEE conference research paper, citations, and algorithmic architecture for this project.