Parallel algorithms for target tracking on multi-coreplatform with mobile LEGO robots
Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
The aim of this master thesis was to develop a versatile and reliable experimentalplatform of mobile robots, solving tracking problems, for education and research.Evaluation of parallel bearings-only tracking and control algorithms on a multi-corearchitecture has been performed. The platform was implemented as a mobile wirelesssensor network using multiple mobile robots, each using a mounted camera for dataacquisition. Data processing was performed on the mobile robots and on a server,which also played the role of network communication hub. A major focus was toimplement this platform in a flexible manner to allow for education and futureresearch in the fields of signal processing, wireless sensor networks and automaticcontrol. The implemented platform was intended to act as a bridge between the idealworld of simulation and the non-ideal real world of full scale prototypes.The implemented algorithms did estimation of the positions of the robots, estimationof a non-cooperating target's position and regulating the positions of the robots. Thetracking algorithms implemented were the Gaussian particle filter, the globallydistributed particle filter and the locally distributed particle filter. The regulator triedto move the robots to give the highest possible sensor information under givenconstraints. The regulators implemented used model predictive control algorithms.Code for communicating with filters in external processes were implementedtogether with tools for data extraction and statistical analysis.Both implementation details and evaluation of different tracking algorithms arepresented. Some algorithms have been tested as examples of the platformscapabilities, among them scalability and accuracy of some particle filtering techniques.The filters performed with sufficient accuracy and showed a close to linear speedupusing up to 12 processor cores. Performance of parallel particle filtering withconstraints on network bandwidth was also studied, measuring breakpoints on filtercommunication to avoid weight starvation. Quality of the sensor readings, networklatency and hardware performance are discussed. Experiments showed that theplatform was a viable alternative for data acquisition in algorithm development and forbenchmarking to multi-core architecture. The platform was shown to be flexibleenough to be used a framework for future algorithm development and education inautomatic control.
Place, publisher, year, edition, pages
2011. , 39 p.
UPTEC F, ISSN 1401-5757 ; 11024
Control Education; LEGO; Benchmarking; Real Data; Bearing-only Target Localisation; Model Predictive Control; Particle Filtering; Sensor Network; Parallelisation; Mobile Robots; Multi-core
IdentifiersURN: urn:nbn:se:uu:diva-155537OAI: oai:DiVA.org:uu-155537DiVA: diva2:426602
Master Programme in Engineering Physics
Pelckmans, KristiaanNyberg, Tomas