  In this paper, we developed a new navigation system, which detects obstacles
in a sliding window with an adaptive threshold clustering algorithm, classifies
the detected obstacles with a decision tree, heuristically predicts potential
collision and finds optimal path with a simplified Mophin algorithm. This
system has the merits of optimal free-collision path, small memory size and
less computing complexity, compared with the state of the arts in robot
navigation. The experiments on simulation and a robot for eight scenarios
demonstrate that the robot can effectively and efficiently avoid potential
collisions with any static or dynamic obstacles in its surrounding environment.
