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About

James P. Wilson is a robotics and autonomous systems researcher whose work sits at the intersection of underwater human-robot interaction, motion planning, and computer vision. His most recognized contribution lies in developing intuitive, non-intrusive communication frameworks for autonomous underwater vehicles (AUVs), particularly systems that allow human divers to interact with robots through natural hand gestures rather than cumbersome waterproof keyboards or joystick controllers. His 2019 paper on diver gesture recognition using deep learning has garnered 17 citations, while his DARE framework — explored across multiple publications — has collectively advanced the field of diver action recognition using multi-channel convolutional neural networks, accumulating an additional 16 citations across iterations. Beyond underwater interaction, Wilson has made meaningful contributions to autonomous navigation, including his ε⁺ algorithm for energy-constrained coverage path planning in unknown environments (12 citations) and the SMARRT system for real-time replanning amid dynamic obstacles. Together, these works reflect a cohesive research vision: enabling autonomous vehicles to operate more intelligently and collaboratively in complex, real-world environments. His contributions are particularly valuable for students and practitioners working at the frontier of marine robotics and autonomous systems.