Michael Gillham
Papers
8
Total Citations
63
H-Index
5
About
Michael Gillham is a researcher specializing in assistive robotics, autonomous navigation, and human-machine interaction, with a particular focus on developing intelligent systems for powered wheelchair users. His work addresses one of the fundamental challenges in assistive technology: creating systems that enhance user safety and mobility without undermining individual autonomy and control. Gillham's most significant contributions center on collision avoidance, doorway detection, and real-time path planning for smart robotic wheelchairs. His development of the Dynamic Localized Adjustable Force Field method (2015, 13 citations) represents a notable advance in non-holonomic mobile robotics, providing adaptive navigation assistance that preserves user agency. Complementing this, his application of weightless neural networks to real-time environmental recognition — including doorway detection, junction identification, and floor surface classification — demonstrates a commitment to computationally efficient solutions suitable for real-world deployment (2012–2013, up to 12 citations each). Beyond wheelchairs, Gillham has extended his expertise to semi-autonomous trajectories applicable to military ground platforms and teleoperated robots, broadening the impact of his research. With a body of work spanning simulation environments, localization techniques, and 3D obstacle avoidance, his research consistently prioritizes practical, human-centered design in complex, dynamic environments — making meaningful contributions to the independence and quality of life of disabled and elderly users.
Research Focus
Key Achievements
Top Papers
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- 4Assistive Trajectories for Human-in-the-Loop Mobile Robotic Platforms6 citations · 2015
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- 6Highly efficient localisation utilising weightless neural systems5 citations · 2012
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