Patrick McEnroe
Papers
1
Total Citations
3
H-Index
1
About
Patrick McEnroe is pioneering the intersection of edge artificial intelligence and autonomous drone navigation. His research focuses on enabling Unmanned Aerial Vehicles (UAVs) to perform complex obstacle avoidance in real-time by moving computation from the cloud to the device itself. His most-cited work, "Towards Faster DRL Training: An Edge AI Approach for UAV Obstacle Avoidance by Splitting Complex Environments," introduces a novel framework that partitions intricate environments to accelerate deep reinforcement learning training directly on edge hardware. This approach addresses a critical bottleneck in drone autonomy—the latency and bandwidth constraints of cloud-dependent systems. By demonstrating that UAVs can learn and react to obstacles on the fly using onboard processing, McEnroe’s work has significant implications for applications ranging from package delivery to search-and-rescue. With 3 citations already in its first year, this paper is quickly establishing him as a rising voice in edge computing and robotics. His contributions are helping to define a new paradigm where intelligence is not just connected, but truly embedded in the machines that navigate our world.
Research Focus
Key Achievements
Top Papers
- 1