Robotic Sniffer Dog Project
- Members: Jon Timmis, Andy Tyrrell, James Hilder, Nick Owens
- Funded by: DSTL
- Start Date: January 2010
The Robotic Sniffer Dog project is a project funded by DSTL which attempts to improve the accuracy and reliability of automatic chemical agent detection using a robotic platform. The project was initial funded due to the success of the Receptor Density Algorithm (RDA), an immune-inspired algorithm developed by Dr. Nick Owens as part of his PhD research project, at detecting anomalies within a Proton-Transfer Reaction Mass Spectrometry data-set in a competition organised by DSTL at the ICARIS 2009 Conference. The RDA is inspired by the signalling mechansims found within T-cells, and is developed from models of the immunological T-cell and the T-cell recport's ability to contribute to T-cell discrimination.
Following the successful competition results seen by the RDA, DSTL funded three short work-periods with the goal of adapting the RDA to be used on an embedded robotic platform. The sensor used was a Smith's Detection CAM device, mounted on a Pioneer 4AT 4-wheeled robot. The platform was navigated across a noisy environment and successfully descriminated the target substance, aerosol-based Deep Heat, from a number of other VOCs.
Further work was then carried out which allows the automatic optimisation of RDA parameters for different sensors and environments, with efforts made to accelerate the process through the use of GPGPU acceleration using NVidia's CUDA programming language. Funding has now been allocated for a single 3-year PhD studentship to further the project, enhancing the algorithm and using new sensors and the group's new 4-wheel off-road Jaguar robotic platform.
Paper: Parameter Optimisation in the Receptor Density Algorithm James A. Hilder, Nick D. L. Owens, Peter J. Hickey, Stuart N. Cairns, David P. A. Kilgour, Jon Timmis and Andy Tyrrell (Artificial Immune Systems, Lecture Notes in Computer Science, 2011, Volume 6825/2011)