Most of work on motion of mobile robots is to generate plans for avoiding obstacles or perform some meaningful and useful actions. In modern robot theatres and entertainment robots the motions of the robot are scripted and thus the performance or behavior of the robot is always the same. In this work we want to propose a new approach to robot motion generation. We want our robot to behave more like real people. People do not move in mechanical way like robots. When a human is supposed to execute some motion, these motions are similar to one another but always slightly or not so slightly different. We want to reproduce this property based on the introduced by us new concept of probabilistic regular expression, a method to describe sets of interrelated similar actions instead of single actions. Our goal is not only to create motions for humanoid robots that will look more naturally and less mechanically, but also to program robots that will combine basic movements from certain library in many different and partially random ways. While the basic motions were created ahead of time, their combinations are specified in our new language. Although now our method is only for motions and does not take inputs from sensors into account, in future the language can be extended to input/output sequences, thus the robot will be able to adapt the motion in different ways, to some sets of sequences of input stimuli. The inputs will come from sensors, possibly attached to limbs of controlling humans from whom the patterns of motion will be acquired.

Lately, personal and entertainment robotics are becoming more and more common. In this thesis, the application of entertainment robots in the context of a Robot Theatre is studied. Specifically, the thesis focuses on the synthesis of expressive movements or animations for the robot performers (Robot Actors). The novel paradigm emerged from computer animation is to represent the motion data as a set of signals. Thus, preprogrammed motion data can be quickly modified using common signal processing techniques such as multiresolution filtering and spectral analysis. However, manual adjustments of the filtering and spectral methods parameters, and good artistic skills are still required to obtain the desired expressions in the resulting animation. Music contains timing, timbre and rhythm information which humans can translate into affect, and express the affect through movement dynamics, such as in dancing. Music data is then assumed to contain affective information which can be expressed in the movements of a robot. In this thesis, music data is used as input signal to generate motion data (Dance) and to modify a sequence of pre-programmed motion data (Scenario) for a custom-made Lynxmotion robot and a KHR-1 robot, respectively. The music data in MIDI format is parsed for timing and melodic information, which are then mapped to joint angle values. Surveys were done to validate the usefulness and contribution of music signals to add expressiveness to the movements of a robot for the Robot Theatre application.

M. Tech. (Department of Information and Communication Technology, Faculty of Applied and Computer Sciences), Vaal University of Technology. / Since their emergence in the 1990’s, recommendation systems have transformed the intelligence of both the web and humans. A pool of research papers has been published in various domains of recommendation systems. These include content based, collaborative and hybrid filtering recommendation systems. Recommendation systems suggest items to users and their principal purpose is to increase sales and recommend items that are predicted to be suitable for users. They achieve this through making calculations based on data that is available on the system. In this study, we give evidence that the research on group recommendation systems must look more carefully at the dynamics of group decision-making in order to produce technologies that will be more beneficial for groups based on the individual interests of group members while also striving to maximise satisfaction. The matrix factorization algorithm of collaborative filtering was used to make predictions and three movie recommendation for each and every individual user. The three recommendations were of three highest predicted movies above the pre-set threshold which was three. Thereafter, four virtual groups of varied sizes were formed based on four highest predicted movies of the users in the dataset. Plurality voting strategy was used to achieve this. A publicly available dataset based on Group Recommender Systems Enhanced by Social Elements, constructed by Lara Quijano from the Group of Artificial Intelligence Applications (GIGA), was used for experiments. The developed recommendation system was able to successfully make individual movie recommendations, generate virtual groups, and recommend movies to these respective groups. The system was evaluated for accuracy in making predictions and it was able to achieve 0.7027 MAE and 0.8996 RMSE. This study was able to recommend to virtual groups to enable social network group members to engage in discussions of recommended items. The study encourages members in engaging in similar activities in their respective physical locations and then discuss on social network.