Edward Stow

Papers

Total Citations

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About

Edward Stow is a robotics researcher whose work centers on path planning and energy-efficient onboard computation for autonomous navigation. His most influential contribution is the development of **PathBench**, a systematic benchmarking framework for comparing classical and learning-based path planning algorithms. This work, published in 2022 with 12 citations, provides a standardized methodology for evaluating algorithms like wavefront and rapidly exploring random trees against emerging machine learning approaches, offering critical insights for researchers and engineers selecting navigation solutions. Stow also pioneers the use of **focal-plane sensor-processors (FPSPs)** for robot navigation. In his 2022 paper on compiling CNNs with Cain, he demonstrates how these ultra-low-power, high-frame-rate cameras can execute neural network inference directly on the image sensor, enabling real-time edge computing for resource-constrained robots. This work addresses a fundamental challenge in autonomous systems: achieving intelligent perception without draining battery life or requiring bulky hardware. By bridging algorithm benchmarking and novel hardware acceleration, Stow is shaping the future of efficient, intelligent mobile robotics, making his research essential reading for anyone working at the intersection of computer vision, embedded systems, and autonomous navigation.