Maxwell Lefebvre
Papers
2
Total Citations
12
H-Index
2
About
Maxwell Lefebvre is an emerging researcher at the intersection of robotics, reinforcement learning, and wireless energy systems. His work focuses on solving critical optimization problems in far-field wireless power transfer, specifically for powering the growing ecosystem of Internet of Things (IoT) devices. Lefebvre’s key contribution lies in developing intelligent, mobile charging solutions. In his most cited work, "Optimal Path Planning for Wireless Power Transfer Robot Using Area Division Deep Reinforcement Learning" (9 citations), he pioneered a method where a mobile robot equipped with a Radio-Frequency transmitter uses deep reinforcement learning to efficiently patrol and charge nearby IoT devices. He further refined this approach in "Optimize Mobile Wireless Power Transfer by Finite State Machine Reinforcement Learning" (3 citations), demonstrating a novel application of finite state machines to guide the robot’s charging strategy. By replacing static charging stations with adaptive, learning-driven robots, Lefebvre’s research directly addresses the scalability and energy autonomy challenges of dense IoT networks. His work represents a significant step toward truly self-sustaining wireless sensor systems, making him a notable voice in the future of automated energy delivery.
Research Focus
Key Achievements
Top Papers
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