Hierarchical Reactive Control for Soccer Playing Humanoid Robots

What drives thousands of researchers worldwide to devote their creativity and energy t

Robot Controller Program for SICK Robot Day 2018 Competition

Company SICK is hosting a Robotday SICK 2018 competition in October of 2018. The tasks of the competition is to make an autonomous robot with the goal of picking up balls from an autonomous, moving robot and collecting them into a basket. My task was to design and implement a robot controller program (hereafter referred to as "SICKcon") in the way that allows asynchronous development of independent components, such as computer vision, drive controller and mechanic hand design. Unfortunately, some parts were not finished in time and I had to improvise for testing purposes, so a big part is simulated. Evaluation is based completely on simulated environment and the robot was never tested with real components. The goal of enabling asynchronous development was thus partly reached as I was not able to test how my drive controller would react in real world. However, the goal of adaptability was reached better due to hierarchy of goals and implementation with many levels which allows partial changes as needed

Designing Agent Behavior with the Extensible Agent Behavior Specification Language XABSL

Specific behavior description languages prove to be suitable replacements to native programming language like C++ when the number and complexity of behavior patterns of an agent increases. The XML based Extensible Agent Behavior Specification Language (XABSL) also tries to simplify the process of specifying complex behaviors and supports the design of both very reactive and long term oriented behaviors

A Framework for Group Modeling in Agent-Based Pedestrian Crowd Simulations

Pedestrian crowd simulation explores crowd behaviors in virtual environments. It is extensively studied in many areas, such as safety and civil engineering, transportation, social science, entertainment industry and so on. As a common phenomenon in pedestrian crowds, grouping can play important roles in crowd behaviors. To achieve more realistic simulations, it is important to support group modeling in crowd behaviors. Nevertheless, group modeling is still an open and challenging problem. The influence of groups on the dynamics of crowd movement has not been incorporated into most existing crowd models because of the complexity nature of social groups. This research develops a framework for group modeling in agent-based pedestrian crowd simulations. The framework includes multiple layers that support a systematic approach for modeling social groups in pedestrian crowd simulations. These layers include a simulation engine layer that provides efficient simulation engines to simulate the crowd model; a behavior-based agent modeling layers that supports developing agent models using the developed BehaviorSim simulation software; a group modeling layer that provides a well-defined way to model inter-group relationships and intra-group connections among pedestrian agents in a crowd; and finally a context modeling layer that allows users to incorporate various social and psychological models into the study of social groups in pedestrian crowd. Each layer utilizes the layer below it to fulfill its functionality, and together these layers provide an integrated framework for supporting group modeling in pedestrian crowd simulations. To our knowledge this work is the first one to focus on a systematic group modeling approach for pedestrian crowd simulations. This systematic modeling approach allows users to create social group simulation models in a well-defined way for studying the effect of social and psychological factors on crowd’s grouping behavior. To demonstrate the capability of the group modeling framework, we developed an application of dynamic grouping for pedestrian crowd simulations

Applying reinforcement learning in playing Robosoccer using the AIBO

"Robosoccer is a popular test bed for AI programs around the world in which AIBO entertainments robots take part in the middle sized soccer event. These robots need a variety of skills to perform in a semi-real environment like this. The three key challenges are manoeuvrability, image recognition and decision making skills. This research is focussed on the decision making skills ... The work focuses on whether reinforcement learning as a form of semi supervised learning can effectively contribute to the goal keeper's decision making when a shot is taken." -Master of Computing (by research