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About

Lucas Cagle is a researcher specializing in autonomous systems, sensor fusion, and multi-agent robotics, with contributions spanning industrial automation, collision avoidance, and swarm intelligence. His most recognized work, "LiDAR and Camera Detection Fusion in a Real-Time Industrial Multi-Sensor Collision Avoidance System" (2018), has accumulated 124 citations and stands as a foundational contribution to the field of real-time safety systems. In this research, Cagle demonstrated how combining LiDAR and camera data can significantly enhance collision avoidance performance in industrial and advanced driver-assistance system (ADAS) contexts — a practical breakthrough with direct implications for autonomous vehicle safety and industrial automation workflows. More recently, Cagle has extended his focus to the coordination of autonomous ground vehicle swarms, exploring how communication constraints shape decentralized control strategies. His 2023 paper on blended leader-follower and artificial potential field approaches, enriched by biologically inspired interactions, reflects a growing interest in scalable, robust multi-robot systems capable of operating in challenging environments. Together, his body of work illustrates a consistent drive to solve real-world autonomy challenges — from protecting human workers on industrial floors to orchestrating intelligent, self-organizing vehicle teams — making him a valuable voice in the evolving autonomous systems research community.