Predictive Collision Avoidance

Predictive collision avoidance focuses on enabling autonomous systems, from robots to vehicles, to safely navigate dynamic environments by anticipating and preventing collisions. Current research emphasizes real-time obstacle avoidance using techniques like neural networks (including bio-inspired architectures), Kalman filters for multi-object tracking, and model predictive control for trajectory optimization, often incorporating sensor data such as LiDAR. These advancements are crucial for improving the safety and efficiency of autonomous systems in diverse applications, ranging from industrial robotics and autonomous driving to search and rescue operations and aerial surveillance.