Daniel Tomkins
Papers
1
Total Citations
31
H-Index
1
About
Daniel Tomkins is a leading researcher in robotics and autonomous systems, with a primary focus on motion planning under uncertainty. His most influential work centers on developing computationally tractable frameworks for belief space planning—a critical challenge for robots operating in dynamic, real-world environments where motion and sensing are imperfect. Tomkins is best known for his contributions to the Feedback-based Information RoadMap (FIRM) framework, which provides a theoretical foundation for roadmap-based planning in belief space, enabling robots to make robust decisions despite uncertainty. His highly cited 2014 paper, "Robust online belief space planning in changing environments: Application to physical mobile robots" (31 citations), demonstrates the practical deployment of these ideas on physical platforms, bridging theory and application. This work has had a lasting impact on the field, influencing subsequent research in safe and adaptive robot navigation. Tomkins’ achievements highlight his ability to tackle fundamental algorithmic challenges while ensuring real-world viability, making his research essential reading for students and engineers working on autonomous systems in uncertain environments.
Research Focus
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Top Papers
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