Intelligent Perception and Control of Unmanned Systems 

Summary:

Advanced perception and control methods are important for performances such as the performances of safety, reliability, security and robustness in intelligent unmanned systems. Advances in artificial intelligence, sensor fusion, and edge computing have enabled these systems to operate in complex, dynamic environments with minimal human intervention. However, achieving reliable autonomy demands breakthroughs in accurate detection, real-time planning, adaptive control, and multi-agent coordination, particularly under uncertainties like unpredictable obstacles, communication latency, or resource constraints. This session will explore the scope of intelligent perception, autonomous planning and control through interdisciplinary lenses, covering topics such as AI-driven path optimization, reinforcement learning for adaptive behaviors, model-based control, data-driven control, swarm intelligence, human-in-the-loop control architectures, and resilience against cyber-physical threats. By uniting experts from robotics, computer science, control theory, and industry, it is significant to catalyze innovations that push the boundaries of unmanned systems, enabling them to operate seamlessly in both structured and unstructured environments while addressing societal and technical challenges. The goal is to bridge theoretical innovations with practical applications, fostering collaboration among researchers, engineers, and policymakers to develop robust and scalable solutions for intelligent unmanned systems.