Peter Silson
Papers
10
Total Citations
115
H-Index
6
About
Peter Silson is a robotics researcher whose work has made meaningful contributions to autonomous mobile robot navigation, sensor fusion, and localization — some of the most challenging problems in modern robotics. His research spans deterministic and probabilistic approaches, with a particular focus on integrating interval analysis with nonlinear filtering techniques such as the Unscented Kalman Filter (UKF) and Extended Kalman Filter (EKF) to achieve reliable robot localization in complex environments. Silson's most cited work, "Robust Sensor-Based Navigation for Mobile Robots" (2008, 37 citations), demonstrates how interval analysis can enable deterministic localization without initialization dependency, a notable advancement over traditional probabilistic methods. His 2004 paper on sensor fusion using interval analysis and UKF (32 citations) further established his approach of combining inertial sensors, encoders, and ultrasonic sensors to enhance navigational accuracy. He has also explored fuzzy logic as an adaptive mechanism within these frameworks, adding interpretability and robustness to sensor fusion pipelines. Beyond localization, Silson extended his expertise to robotic manipulation, developing real-time trajectory generation algorithms for robot arms operating in confined and hazardous environments. With over 115 cumulative citations across a focused body of work, his research remains a valuable reference for engineers and researchers developing reliable autonomous robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Robust Sensor-Based Navigation for Mobile Robots37 citations · 2008
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- 6Feature based robot navigation: using fuzzy logic and interval analysis6 citations · 2005
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- 10Robot localisation using interval analysis2 citations · 2004