Annual Report 2002 - Ãrebro universitet

More documents

Recommendations

Info

Annual Report 2002 59 Project: Human-Robot Cooperation Project leader: Tom Duckett Project staff: Grzegorz Cielniak Funding: KK and Faculty funding. Cooperation: research visit by Grzegorz Cielniak to the AI Lab, University of Freiburg, Germany as a Marie Curie Fellow (Oct 2002 – Jan 2003), with Prof. Wolfram Burgard. Synopsis: The ability to interact with people is an important requirement for robots, which operate in populated environments. In tasks such as cleaning, housekeeping, rehabilitation, entertainment, inspection and surveillance, so-called service robots need to communicate and cooperate with people. To enable this interaction, the robot needs to know how many people there are in the neighbourhood, their position, and who they are (the three fundamental problems of recognition, tracking and identification). This project investigates embedded machine learning algorithms for robot-human cooperation. In current work, we are integrating different methods for recognition, tracking and identification of people by a mobile robot using vision and laser range-finder sensors. Possible themes for future research include recognition, interpretation and prediction of human behaviour; and development of natural language or gesture-based interfaces for communicating tasks to robots. Results in 2002: - Experiments on human identification using vision. - Development of a learning system for recognizing, tracking and identifying several humans in an office environment using Abstract Hidden Markov Models (AHMMs), paper submitted to IJCAI’03. Future developments: - Scaling up of the AHMM system, and improved techniques for human identification, e.g., incorporating face recognition, gait recognition, etc. - Development of a complete application “the Robotic Security Guard”, which should be capable of recognizing AASS members and tracking their movements, while raising the alarm if unauthorized persons enter the environment. - Licentiate thesis: expected 2003 (Grzegorz Cielniak) - PhD-thesis: expected 2005 (Grzegorz Cielniak)
60 AASS – Center for Applied Autonomous Sensor Systems Project: Simultaneous Localization and Map Building Project leader: Tom Duckett Project staff: Tom Duckett Funding: KK and Faculty funding. Cooperation: Artificial Intelligence Group, Manchester University, UK, with Dr. Stephen Marsland and Dr. Jonathan Shapiro. Synopsis: This project addresses the problem of navigation by mobile robots operating in large, real world environments, which have not been modified for the purpose of robot navigation. Maps are essential for mobile robots navigating in complex environments, being needed for selflocalisation, path planning and interaction with humans. To navigate in unknown environments, a self-governing robot is faced with a fundamental dilemma: to explore and learn maps of uncharted territory, the robot needs to know its location, but in order to know its location, the robot needs a map. Most existing solutions to the problem of simultaneous localisation and mapping (SLAM) work by decoupling the localisation and mapping processes, assuming that the data association problem (i.e., landmark identification) is already solved when measurements are updated into the map. However, this assumption is bound to fail eventually for complex environments. This project investigates robust SLAM algorithms which take into account both the measurement uncertainty (e.g., due to sensor noise) and identification uncertainty (e.g., due to perceptual aliasing, environmental dynamics, sensor noise, etc.). Results in 2002: - Article on map learning published in Autonomous Robots Journal, based on work carried out in cooperation with Manchester University. - Development of a new approach for SLAM based on genetic algorithms, paper submitted to IEEE ICRA’03. Future developments: - Planned research visit by Udo Frese, a Ph.D. student from DLR (German Aerospace Center), Germany as a Marie Curie fellow in January-March 2003. - Development of new SLAM algorithms based on techniques for statistical analysis and global optimization. An example of a learned map
Page 1 and 2:
Annual Report 2002 Örebro universi
Page 3 and 4:
2 AASS - Center for Applied Autonom
Page 5 and 6:
Page 7 and 8:
Page 9 and 10: 8 AASS - Center for Applied Autonom
Page 11 and 12: 10 AASS - Center for Applied Autono
Page 59: 58 AASS - Center for Applied Autono
Page 69: 68 AASS - Center for Applied Autono
show all

Annual Report 2002 - Ãrebro universitet

Create successful ePaper yourself

Delete template?

Save as template?

Annual Report 2002 - Ãrebro universitet