  Autonomously driving vehicles must be able to navigate in dynamic and
unpredictable environments in a collision-free manner. So far, this has only
been partially achieved in driverless cars and warehouse installations where
marked structures such as roads, lanes, and traffic signs simplify the motion
planning and collision avoidance problem. We are presenting a new control
approach for car-like vehicles that is based on an unprecedentedly fast-paced
A* implementation that allows the control cycle to run at a frequency of 30 Hz.
This frequency enables us to place our A* algorithm as a low-level replanning
controller that is well suited for navigation and collision avoidance in
virtually any dynamic environment. Due to an efficient heuristic consisting of
rotate-translate-rotate motions laid out along the shortest path to the target,
our Short-Term Aborting A* (STAA*) converges fast and can be aborted early in
order to guarantee a high and steady control rate. While our STAA* expands
states along the shortest path, it takes care of collision checking with the
environment including predicted states of moving obstacles, and returns the
best solution found when the computation time runs out. Despite the bounded
computation time, our STAA* does not get trapped in corners due to the
following of the shortest path. In simulated and real-robot experiments, we
demonstrate that our control approach eliminates collisions almost entirely and
is superior to an improved version of the Dynamic Window Approach with
predictive collision avoidance capabilities.
