Thomas G. Goodsell

Charles River Analytics (United States), Utah State University

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About

Thomas G. Goodsell is a mobile robotics researcher whose work focuses on enabling autonomous navigation in real-world, often unstructured environments. His research spans computer vision, path planning, and human-robot interaction, with a particular emphasis on overcoming the perceptual and mobility challenges that limit practical robot deployment. Goodsell’s most cited work, “Single Camera Stereo for Mobile Robot Surveillance” (2005, 9 citations), introduced a cost-effective stereo vision method using a single camera, a key contribution for obstacle detection and navigation in settings like parking lots. He also advanced mobility planning for omni-directional vehicles (1999, 4 citations) by developing a grammar-based abstraction to reduce the computational complexity of path planning in natural terrains. Additionally, his work on sign detection (2003, 3 citations) tackled the critical problem of enabling robots to read and interpret human-readable signs, a major step toward intuitive tasking and landmark-based localization. Goodsell’s research on ego-location and situational awareness (2003, 2 citations) further addressed the challenges of semistructured environments, bridging the gap between highly structured labs and the messy, dynamic spaces where robots are most needed.