Faculty of Computing

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Modeling Vision Utility for Side-by-Side Navigation of Robot-Human Pairs
(IEEE, 2019-10-17) Jayawardena, C; Kehelella, P; De Silva, R
Side-by-side robot navigation has significant direct benefits; especially when robotic wheelchairs are used. In addition to navigation issues faced by any mobile robot, side-by-side navigation has some other challenges as well. Maintaining side-by-side formation, avoiding collisions with minimal disturbance to the side-by-side formation, and maintaining the optimal social relationship are some of those challenges. This paper presents a novel decision making model for human-robot side by side navigation. The development of the model was based on observations of real-world human behaviour and data collections done through a user study. The developed model was calibrated and tested using a simulator as-well-as a laboratory experiment.
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A decision making model for optimizing social relationship for side-by-side robotic wheelchairs in active mode
(IEEE, 2016-06-26) Nguyen, V; Jayawardena, C
Nowadays, not only the ability to navigate to destinations but also the ability to navigate in harmony in crowded environments is a crucial feature that helps mobile robots to be accepted in daily routines of humans. In the case of wheelchairs that can move in parallel to a caregiver (side-by-side), this requirement is more complex as the navigation should be comfortable to the wheelchair user, caregiver, as-well-as surrounding people. This study aims to propose a novel decision model for robotic wheelchairs for passing obstacles when moving side-by-side with a human. The proposed model was developed by studying human behavior.
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Approximate decision making by natural language commands for robots
(IEEE, 2006-11-06) Watanabe, K; Jayawardena, C; Izumi, K
Inferring the correct meaning of natural language commands, as judged by the person who issues commands, is mandatory for natural language commanded robotic systems. There have been some successful research on this; but one of the important and related aspects has not been addressed, i.e. the possibility of learning from natural language commands. Since natural language commands are generated by human users, they contain valuable information. Nevertheless, the learning from such commands, as well as the interpretation of them face many challenges due to the inherent subjectiveness of natural languages. In this paper, we propose a decision making process for natural language commanded robots which is influenced by certain characteristics of human decision making process. The proposed concept is demonstrated with an experiment conducted using a robotic manipulator. First, the robot is controlled with natural language commands to perform some pick and place operations during which the robot builds a knowledge base. After learning, the robot is capable of performing approximately similar tasks by making approximate decisions with the gained knowledge. For the decision making a probabilistic neural network is used

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