· · · Institutional Repository

The particle filter is a Bayesian estimation technique based on Monte Carlo simulations. The non-parametric nature of particle filters makes them ideal for non-linear non-Gaussian systems. This greater filtering accuracy, however, comes at the price of increased computational complexity which limits their practical use for real-time applications. This thesis presents an attempt to enable real-time particle filtering for complex estimation problems using modern GPU hardware. We propose a GPU-based generic particle filtering framework which can be applied to various estimation problems. We implement a real-time estimation application using this particle filtering framework and measure the estimation error with different filter parameters. Furthermore, we present an in-depth performance analysis of our GPU implementation followed by a number of optimisations in order to increase implementation efficiency.

Communication libraries that are created for Multi Agent Systems and Distributed Robotics Systems are generally application specific and do not address many systems with different capabilities. The purpose of this project is to design and implement a software communication library for Distributed Robotics Systems not only to meet the needs of Distributed Robotics Group, but also to address more general Multi Agent Systems. The Distributed Robotics Library is modeled by the standard 7 Layer OSI Reference Model. The physical transmission and medium access are standardized by WLAN standards. For networking IPv4, and for the transport control TCP and UDP protocols are used. For identification of the robots in the network, automatic advertisement broadcasting technique is used. Connection request/accept technique is used to start server/client communication between the robots. To test the Distributed Robotics Library, 5 Asus Eee PC's are used to represent the robots. For testing several functions, an application is implemented to address the clock synchronization problem. During the tests, a message relaying function is implemented and added as an extension to the library. The Distributed Robotics Library is also tested in Unix and its portability is verified.

Mobile robots that communicate and cooperate to achieve a common task have been the subject of an increasing research interest in recent years. These possibly heterogeneous groups of robots communicate locally via a communication network and therefore are usually referred to as robotic networks. Their potential applications are diverse and encompass monitoring, exploration, search and rescue, and disaster relief. From a research standpoint, in this thesis we consider specific aspects related to the foundations of robotic network algorithmic development: distributed estimation, control, and optimization. The word “distributed” refers to situations in which the cooperating robots have a limited, local knowledge of the environment and of the group, as opposed to a “centralized” scenario, where all the robots have access to the complete information. The typical challenge in distributed systems is to achieve similar results (in terms of performance of the estimation, control, or optimization task) with respect to a centralized system without extensive communication among the cooperating robots. In this thesis we develop effective distributed estimation, control, and optimization algorithms tailored to the distributed nature of robotic networks. These algorithms strive for limiting the local communication among the mobile robots, in order to be applicable in practical situations. In particular, we focus on issues related to nonlinearities of the dynamical model of the robots and their sensors, to the connectivity of the communication graph through which the robots interact, and to fast feasible solutions for the common (estimation or control) objective.