Papers
14
Total Citations
147
H-Index
7
About
I.P.W. Sillitoe is a researcher whose work sits at the intersection of mobile robotics, machine learning, and autonomous navigation. Over more than a decade of sustained inquiry, Sillitoe has made significant contributions to two principal areas: intelligent path planning for mobile robots and adaptive learning systems for autonomous navigation. His most-cited work, "Robot Navigation by Waypoints" (2008, 31 citations), and its companion study on waypoint-based navigation, demonstrate his innovative approach to blending reactive and deliberative navigation strategies, enabling robots to build experiential knowledge of their environments. Particularly notable is his development of genetic algorithm-based path planners capable of rapidly computing near-optimal routes through dynamic, obstacle-filled environments — work that attracted 28 citations and addressed real-world complexity through elegant heuristic design. Running parallel to this, Sillitoe pioneered incremental and batch-mode decision tree learning methods tailored for embedded robotic systems, advancing the field of on-board machine learning as early as 1994. His longitudinal research record, spanning foundational sonar-based environment mapping to sophisticated evolutionary computation techniques, reflects a researcher deeply committed to making autonomous robots both smarter and more practically deployable in unpredictable real-world settings.
Research Focus
Key Achievements
Top Papers
- 1Robot Navigation by Waypoints31 citations · 2008
- 2Mobile Robot Path Planning in Dynamic Environments28 citations · 2007
- 3Genetic-based Mobile Robot Path Planning using Vertex Heuristics21 citations · 2006
- 4Waypoint-based Mobile Robot Navigation16 citations · 2006
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- 9Efflcient incremental decision tree generation for embedded applications5 citations · 2005
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