Collision Avoidance Capability

Collision avoidance capability research focuses on enabling autonomous systems, such as robots and UAVs, to safely navigate complex and dynamic environments. Current approaches leverage diverse methods including reinforcement learning (with various reward function optimizations and masking techniques), differentiable optimization coupled with generative models, and classical algorithms like Rapidly-exploring Random Trees (RRT) and Velocity Obstacles (VO). These advancements are crucial for improving the safety and reliability of autonomous systems across various applications, from aerial cinematography and industrial robotics to autonomous driving and air traffic management.