Intelligent Robotics

This page contains a summary of ongoing and finished research projects in the field of intelligent robotics here at the department of Computing Science, Umeň University. If you have comments or questions regarding any of these projects, do not hesitate to contact the project leader Thomas Hellström.

Learn From Demonstration on Kompai Robot

The imitation technique is heavily utilized within robotics, in this case often denoted Learning From Demonstration (LFD) or Imitation Learning (IL). A human demonstrates a wanted behavior by tele-operating a robot. In this research, a robot will be equipped with a set of parameterized high-level behaviors. Models and techniques for ways in which behaviors can be combined into sequences will be developed. By recognizing a behavior on-the-fly during a demonstration, a shared control system can be constructed. In this way, the robot may take over control already during the demonstration. The human will be relieved of the hard task of tele-operating but may, if needed, interfere and correct the robot’s control signals.

Benjamin Fonooni



System analysis using discrete event simulation

With continued development of forest machines the operators risk becoming a bottleneck, due to the ever increasing pace they have to work in. Different kind of aids for the operators will become necessary to lessen the workload. Development of autonomous forest machines aims at meeting this demand, and autonomous forwarders are considered to be a commercially profitable product scenario. The aim of this study is to validate this claim and check under what conditions autonomous forwarders could be profitable. This is done by using a discrete event simulator able to simulate the work flow, and thereby productivity, of future autonomous systems and comparing the results with the machines currently used in forestry.

Ola Ringdahl

Forest machine autonomous navigating

This project is intended to develop software and hardware to be mounted on a full-sized forest machine. The goal is to make the machine travel without a driver along a predetermined path in a forest. Among others, the research includes algorithms for "path tracking", as well as detecting, locating, and avoiding obstacles.

More information is availible on the project's webpage.

Ola Ringdahl

Behavior and Task Learning from Demonstration

A popular method for teaching robots simple behaviors involves a human demonstrating a behavior via remote control, or by having the robot observe the human's movements. In this project we endeavour to build robots that can learn new bahvior out of already learned or pre-programmed primitives. Teaching takes place by data mining of sensor data and control commands.

More information is available on the project's webpage.

Erik Billing

USAR - Urban Search And Resque Robots

A model of a USAR robot that uses 3D camera and ultra sonic sonars to explore the environment and build a map. Obstacles are detected by transformation of 3D data to a floor coordinate system. Too high objects are shown in the user's display by overlaying color coded range data with a grayscale image. Simplified "humans" are detected and their locations memorized. Afterwards, the robot can be told to go to any of the found humans. A collision free path is planned using a watershed algorithm.

Check out the video

Kristoffer Cavellin and Peter Svensson

NAV2000 is a mobile-robot middleware implemented in Java, allowing efficient configuration of the robot's sensors and actuators. It supports, among others, the Pioneer and AmigoBot robots.
More information, and downloadable software are avalible on the project's webpage

Ola Ringdahl

Simulator in the loop for path planning

Many path planning algorithms make simplifying assumptions regarding the robot's geometry or kinematic behavior. Typically the robot is approximated by either a point or a circle, and the robot is assumed to be be able to turn in a predictable way given by simple kinematics equations. This usually works fine for regular indoor robots but is less suitable for large outdoor robots and autonomous vehicles.
This project is aimed at extending a standard path tracking algorithm with a simulator that, in real-time, tries to predict collisions in a window forward in time. This simulation is based on current sensor data giving information about the environment around the vehicle. If a collision is predicted to occur, a path optimization phase is initiated. Variants of the original path is generated and simulated until a feasible path is found. The real vehicle may then continue, now tracking the replanned path.

More information can be found in our journal publication.

Ola Ringdahl

Artificial Evolution (of a Climbing Robot)

Most of current robotic applications are based on the "Classical AI" paradigm. Localisation, Computer Vision, Map Building, Planing, are techniques used to solve a large range of real-world applications. Finding, cutting, and gathering timberwoods belong to them. But which kind of robot could climb a tree and attach a cable on the top of it?

On this kind of problems, Classical AI reveals its limitations. Indeed, it has more to do with agility, real-time adaptivity, and embodied / animal-like intelligence. This project adresses this issue through Artificial Evolution.

More information is available on the project's webpage.

Fabien Lagriffoul

Localization by using a laser scanner

We analyze images from a laser scanner, and determine the shift (position and direction) by comparing two images. The technology can be used for relative localization ("laser odometry") by calculating relative movements between two pictures taken in sequence, and also for absolute localization by comparing a recent picture with pictures from a database.

Arsalan Siddiqui

Identifying and felling trees

A miniature robot equipped with a camera is used for illustrating the idea of a cleaning robot for the forest industry. Our mini-robot operates in-house and can distinguish between various trees and different log shapes, using histogram analyses of snapshots and a small database of various tree types. More information is available on the project's webpage.

Erik Billing

Neural visual odometry

This project is intended to develop technologies for determining a forest vehicle's movements, using cameras pointing downwards to the ground. Techniques for correlating pictures and optical-flow algorithms are used to determine the pose change between two consecutive pictures.

Jan Rudzki

Rock detection

In this project we investigate sensor technologies to detect rocks in mines. Boulders often present a problem when transporting ores in LKAB's subterranean mine shafts. By detecting and then knocking down the boulder, costly work breaks can be avoided.

Erik Billing

Aria Matlab interface

Jonas Borgström

Caro - the Candy Robot

Shafkat Kibria

KiKS - Khepera Simulator

KiKS is an abbreviation for "KiKS is a Khepera Simulator". As the name suggests, KiKS simulates a Khepera robot connected to the computer in a very realistic way and runs under Matlab. You control the simulated khepera using three commands (kiks_kopen, kiks_ksend, kiks_kclose) which work just like the K-team kMatlab modules (kopen.dll, ksend.dll, kclose.dll), so if you are familiar with controlling the khepera robot from Matlab you can instantly start using this simulator. Also, creating programs that can run on both simulated kheperas and real kheperas is extremely easy since the kiks_kopen/kiks_ksend/kiks_close commands work as wrappers for the kMatlab modules - calling kiks_kopen with a negative port number opens up communication with a simulated khepera, and calling kiks_kopen with a positive port number simply redirects the call to kopen.dll.

KiKS started out as my master thesis and has since then been further developed. More information can be found here.

Theodor Storm

Last updated: November 28 2014

Contact: Thomas Hellström, Department of Computing Science.
Webmaster: Erik Billing, Department of Computing Science.