Feedback Motion

Feedback motion planning focuses on designing control algorithms that enable robots to navigate to a goal while reacting to sensor data and environmental changes. Current research emphasizes robust methods, particularly using deep reinforcement learning and graph-based approaches to handle complex environments and uncertainties in sensor measurements, often incorporating techniques like Lyapunov functions and barrier functions for safety guarantees. These advancements are crucial for improving the reliability and safety of autonomous robots in real-world applications, such as mobile robotics and automated navigation systems.