从规则驱动到群智涌现: 多机器人空地协同研究综述

The multi-robot air-ground collaboration system, which is crucial for search and rescue, exploration, and other fields, has garnered significant attention from researchers in recent years. Overcoming challenges related to limited intelligence and weak autonomy in such systems is essential to enhance individual intelligence and strengthen collective collaboration autonomy, thereby accelerating their practical applications. In recent years, with the continuous advancement of artificial intelligence (AI) algorithms in perception and decision-making, such as deep learning and collective intelligence, their applications to air-ground collaborative systems have become a research hotspot. Based on the level of autonomy in air-ground collaboration, this paper summarizes air-ground collaboration efforts at different collaboration levels, ranging from rule-driven approaches to collective intelligence emergence, emphasizing the enhancement of individual intelligence to achieve collective intelligence. Furthermore, this paper constructs the concepts and expands the features of the air-ground collaboration collective intelligence system, and outlines its self-organizing, self-adaptation, self-learning, and continuously evolving qualities. Finally, by listing representative application scenarios, this paper encapsulates the challenges and explores future directions in air-ground collaboration.