Autonomous Ground Navigation

Autonomous ground navigation focuses on enabling robots to safely and efficiently traverse diverse environments, from structured indoor spaces to unstructured off-road terrains. Current research emphasizes robust path planning algorithms, often integrating classical methods like A* with machine learning approaches such as reinforcement learning and Gaussian process regression to handle uncertainty and dynamic obstacles. These advancements are crucial for improving robot safety and efficiency in various applications, including search and rescue, logistics, and exploration, as evidenced by the ongoing development and evaluation of autonomous navigation systems in competitive challenges. A growing focus on risk assessment and the integration of digital twin technologies aims to improve the reliability and safety of these systems in real-world deployments.