**Mobile Robot Localization**

PROBLEM: Mobile robot localization problem is usually defined as a
problem of estimating robot pose relative to the map of its
environment. Depending on the prior knowledge we differentiate two
cases: 1. the pose tracking problem (initial position of a mobile
robot is known), and 2. the global localization problem (robot
position has to be estimated from scratch). The pose tracking
problem can be effectively solved with the classical Kalman
filtering approach, because normal distribution accurately
approximates the probability distribution around the true
robot position. However, in the case of an unknown initial robot
position (global localization problem), assumption about the normal
probability distribution of the robot pose is violated and therefore
more sophisticated algorithms have to be used.

GOAL: Our research is focusing on the design of an efficient
localization algorithms with emphasis on the global localization
problem. Additional problem that may occur during localization is
the so-called "kidnapped robot problem", where the mobile robot is
being teleported to another location. In this case, it is apparent
that the localization algorithm must effectively detect such a
situation and estimate the new robot pose as fast as possible.

METHODOLOGY: The estimation strategy used in our research is based
on the particle filter algorithms. In the context of global
localization, compared to the classical approach based on Kalman
filters, the main advantage of particle filters is their ability to
approximate arbitrary probability distribution. However, since the
particle filter based algorithms are, in general, computationally
demanding we are also investigating how to reduce complexity of the
the robot localization algorithms.

**Tire/Road Friction estimation**

PROBLEM: This topic plays important role in many vehicle control
systems due to the fact that the vehicle behavior is primarily
determined by the forces transferred between the wheels and the
road. Therefore, many vehicle control systems like Anti-lock Brake
System (ABS), Traction Control (TC) system, Electronic Stability
Program (ESP) can be generally considered as control systems whose
goal is efficient control of the amount of forces transferred from
the vehicle tire and the road. Since the actual friction force
cannot be easily measured, it has to be estimated on the basis of
information collected from standard sensors (e.g. wheel speed
encoders).

GOAL: Our research focuses on the design of the tire/road friction
estimators that provide information about the tire/road friction,
either by estimating the actual value of the friction force or by
the road condition parameter. Additionally, a tire/road friction
estimator should cope with the problem of signal and model
uncertainties (measurement noise, modeling uncertainties, time
variability of the process, etc.).

METHODOLOGY: Methodologies used here include optimal filtering such
as Kalman filter (and its sub-variants), Artificial Neural Networks
based methods and Nonlinear methods based on passivity.

**[more...]**