  Mobile robots, especially those driving outdoors and in unstructured terrain,
sometimes suffer from failures and errors in locomotion, like unevenly
pressurized or flat tires, loose axes or de-tracked tracks. Those are errors
that go unnoticed by the odometry of the robot. Other factors that influence
the locomotion performance of the robot, like the weight and distribution of
the payload, the terrain over which the robot is driving or the battery charge
could not be compensated for by the PID speed or position controller of the
robot, because of the physical limits of the system. Traditional planning
systems are oblivious to those problems and may thus plan unfeasible
trajectories. Also, the path following modules oblivious to those problems will
generate sub-optimal motion patterns, if they can get to the goal at all.
  In this paper, we present an adaptive path planning algorithm that is
tolerant to such degraded locomotion conditions. We do this by constantly
observing the executed motions of the robot via simultaneously localization and
mapping (SLAM). From the executed path and the given motion commands, we
constantly on the fly collect and cluster motion primitives (MP), which are in
turn used for planning. Therefore the robot can automatically detect and adapt
to different locomotion conditions and reflect those in the planned paths.
