AN EMERGING TECHNOLOGY LANDSCAPE: THE CONVERGENCE OF ARTIFICIAL INTELLIGENCE AND UNMANNED AERIAL VEHICLES

Artificial intelligence (AI) and unmanned aerial vehicles (UAVs) are each rapidly emerging technologies. The integration of AI into to UAVs is creating a technology convergence that requires exploration and understanding. World-wide patents in the convergence area are an unexplored data source that holds indicators of emergence, and research, development, and innovation trends. This research used a novel combination of tech mining techniques to identify and analyze the emerging technology landscape of the convergence between AI and UAVs. Bibliometric analysis, a cluster map, a network analysis, and a two-mode network analysis are employed to map the landscape. Nineteen emerging technologies were identified. Results indicate deep learning, reinforcement learning, and deep learning techniques are highly connected and the most used AI techniques in the emerging technologies. Computer vision and remote navigation techniques are emergent but not connected, potentially indicating immaturity. G06N-003/08 (computing arrangements based on biological models > neural networks > learning methods), G06V-010/82 (arrangements for image or video recognition or understanding > using pattern recognition or machine learning > using neural networks) and G06N-003/04 (computing arrangements based on biological models > neural networks > architecture) are the most central technology categories in the emerging technologies. Deep reinforcement learning, reinforcement learning, and artificial intelligence are central to categorical emergence based on IPC. Findings contribute to the understanding of the technology landscape of AI and UAV convergence and contribute to the employment of EScript as selection criteria for a patent landscape. The findings can inform policy, regulation, or investment decisions into AI applications for UAVs.