Title :
Autonomous sensor placement
Author :
Knuth, Kevin H. ; Center, Julian L.
Author_Institution :
Depts. of Phys. & Inf., Univ. at Albany, Albany, NY
Abstract :
With an increasing reliance on robotic platforms to perform scientific exploration in remote or hostile environments, it is becoming crucial that robotic systems be able to perform autonomous intelligent sensor placement as well as autonomous experimental design. Such a system requires encoding of scientific knowledge, the ability to make inferences from data, and the ability to identify the most relevant question to ask given both the instrumentpsilas prior knowledge and the issue it is designed to address. This requires implementation of two computational engines: the inference engine and the inquiry engine. Here we demonstrate our first efforts to develop intelligent instruments that rely on autonomous sensor placement.
Keywords :
Bayes methods; Markov processes; Monte Carlo methods; design of experiments; intelligent robots; manipulators; sensors; Bayesian Markov chain Monte Carlo algorithm; autonomous experimental design; autonomous intelligent sensor placement; inference engine; inquiry engine; robotic arm; scientific exploration; scientific knowledge encoding; Bayesian methods; Design for experiments; Engines; Instruments; Intelligent robots; Intelligent sensors; Position measurement; Robot kinematics; Robot sensing systems; Robotics and automation;
Conference_Titel :
Technologies for Practical Robot Applications, 2008. TePRA 2008. IEEE International Conference on
Conference_Location :
Woburn, MA
Print_ISBN :
978-1-4244-2791-8
Electronic_ISBN :
978-1-4244-2792-5
DOI :
10.1109/TEPRA.2008.4686680