Autonomous Miniature

Autonomous miniature vehicles, often scaled-down versions of cars or robots, serve as valuable platforms for researching and developing advanced control and perception algorithms for autonomous systems. Current research emphasizes vision-based navigation using convolutional neural networks (CNNs) and reinforcement learning (RL), often incorporating techniques like model predictive control (MPC) and simulation-to-reality transfer to improve robustness and efficiency. These miniature platforms offer a cost-effective and safe environment for testing novel algorithms, accelerating advancements in autonomous driving and robotics while contributing to a deeper understanding of complex control problems.