Human-Machine Collaborative Systems
Dividing the task that the operator is executing into several subtasks
is one of the key research areas in teleoperative and human-machine
collaborative settings. Hence, segmentation and recognition of
operator generated motions are commonly facilitated to provide
appropriate assistance during task execution. This assistance is
usually provided in a virtual fixture framework where the level of
compliance can be altered online thus improving the performance both
in terms of execution time and overall precision. However, the
fixtures are typically inflexible, resulting in a degraded performance
in cases of unexpected obstacles or incorrect fixture models. In this
project, we are interested in the problem of on-line task tracking and propose
the use of adaptive virtual fixtures that can cope with the
above problems. The operator may remain in each of these subtasks as
long as necessary and switch freely between them. Hence, rather than
executing a predefined plan, the operator has the ability to avoid
unforeseen obstacles and deviate from the model. Here, the
probability that the user is following a certain trajectory (subtask)
can be estimated and used to automatically adjusts the compliance. Thus,
an on-line decision of how to fixture the movement can be provided.
Robot Control
In our lab there is a mobile robot with an attached arm
(manipulator). A natural way to guide the robot would be to take its
"hand" and have it follow you around. Since the end-effector is
equipped with a force sensor giving 6 DOF force-torque measurments
this can be achieved. However, the base is quite unstable and it is
therefore important to carefully design the control system to achieve
a good user experience. The higher bandwidth of the arm can be
utilized to have a decoupling of the applied forces from the robot
motion.
Possible tasks for a project would be:
- Design a coordinated controller for the manipulator/base motion
- Should the controller try to mimic a human?
- Analyze the performance of the controller w.r.t stability, delay etc
- Is the controller robust to changes in parameters?
- Evaluate the controller parameters w.r.t user preferences
- Is the "optimal" controller the one that user preferes?
Programming by Demonstration
Understanding and
interpreting dynamic scenes and activities is a very challenging
problem. In this project, we are interested in developing a system
capable of learning robot tasks from demonstration. Classical robot
task programming requires an experienced programmer and a lot of
tedious work. In contrast, Programming by Demonstration is a flexible
framework that reduces the complexity of programming robot tasks, and
allows end-users to demonstrate the tasks instead of writing code. We
have developed a system capable of learning pick-and place tasks by
manually demonstrating them. Each demonstrated task is described by an
abstract model involving a set of simple tasks such as what object is
moved, where it is moved, and which grasp type was used to move
it. The project will continue with development of visual and action
recognition strategies.
Object Detection in Natural Scenes
Object recognition is one of the major research
topics in the field of computer vision. In robotics, there is
often a need for a system that can locate certain objects in
the environment - the capability which we denote as object
detection. Our current method is especially suitable for detecting objects
in natural scenes, as it is able to cope with problems such as
complex background, varying illumination and object occlusion.
The proposed method uses the receptive field representation
where each pixel in the image is represented by a combination of
its color and response to different filters. Thus, the cooccurrence
of certain filter responses within a specific radius in the image
serves as information basis for building the representation of the
object.
The specific goal of this project is the development of an on-line
learning scheme that is effective after just one training example
but still has the ability to improve its performance with more time
and new examples.
Vision based Simultaneous Localization and Mapping
One key competence for a fully autonomous mobile robot system is the
ability to build a map of the environment from sensor data. Hence,
natural landmark detection and incremental building of consistent maps
for SLAM purposes have been a center point of robotic research for the
last several years. For cases of simple 2D scenarios using laser
scanner or sonar sensors, the SLAM problem is considered to be
solved. However, for large scale and complex environments especially
regarding full 3D SLAM, the prolem is still unsolved. Solving the SLAM
problem with vision as the only external sensor is now the goal of
much of the effort in the area. Monocular vision is especially
interesting as it offers a highly affordable solution with todays
inexpensive webcamera. There are many aspect of the problem and thus a
variety of possible projects: feature selection, delayed vs. undelayed
approach, using structure-from-motion approaches, etc.