Complex Environment

Complex environments, characterized by obstacles, uncertainty, and dynamic elements, pose significant challenges for autonomous systems. Current research focuses on developing robust algorithms and models, such as those based on reinforcement learning, Kalman filtering, and deep neural networks (including CNNs, transformers, and attention mechanisms), to enable effective navigation, perception, and decision-making in these settings. These advancements are crucial for improving the safety and efficiency of robots in various applications, from search and rescue to autonomous driving and industrial automation, and are driving progress in areas like multi-agent systems and human-robot collaboration.