Abstract: A Fourier series based learning control (FSBLC)
algorithm for tracking trajectories of mechanical systems with
unknown nonlinearities is presented. Two processes are introduced to
which the FSBLC with PD controller is applied. One is a simplified
service robot capable of climbing stairs due to special wheels and
the other is a propeller driven pendulum with nearly the same
requirements on control. Additionally to the investigation of learning
the feed forward for the desired trajectories some considerations on
the implementation of such an algorithm on low cost microcontroller
hardware are made. Simulations of the service robot as well as
practical experiments on the pendulum show the capability of the used
FSBLC algorithm to perform the task of improving control behavior
for repetitive task of such mechanical systems.
Abstract: With the aging of the world population and the
continuous growth in technology, service robots are more and more
explored nowadays as alternatives to healthcare givers or personal
assistants for the elderly or disabled people. Any service robot
should be capable of interacting with the human companion, receive
commands, navigate through the environment, either known or
unknown, and recognize objects. This paper proposes an approach
for object recognition based on the use of depth information and
color images for a service robot. We present a study on two of the
most used methods for object detection, where 3D data is used to
detect the position of objects to classify that are found on horizontal
surfaces. Since most of the objects of interest accessible for service
robots are on these surfaces, the proposed 3D segmentation reduces
the processing time and simplifies the scene for object recognition.
The first approach for object recognition is based on color histograms,
while the second is based on the use of the SIFT and SURF feature
descriptors. We present comparative experimental results obtained
with a real service robot.