Dynamic Obstacle

Dynamic obstacle avoidance in robotics focuses on enabling robots to safely and efficiently navigate environments containing moving objects, a crucial challenge for autonomous systems. Current research heavily utilizes reinforcement learning, model predictive control (MPC), and control barrier functions (CBFs) – often in hybrid approaches – to generate robust and safe trajectories, addressing issues like local minima and computational complexity. These advancements are vital for improving the reliability and performance of robots in diverse applications, ranging from autonomous driving and aerial navigation to surgical robotics and human-robot collaboration.