Mobile robot motion by integration of low-level behavior control and high-level global planning

Abstract

This paper presents an efficient strategy for integrating low-level reactive behavior control and high-level global planning for robot motion. In low-level behavior control, robot navigation in unknown environments is performed by behavior fusion using fuzzy logic; while a high-level planning method is used to determine robot motion direction since some information on environments is prior knowledge in many applications. Using behavior fusion by fuzzy logic, a mobile robot is able to directly execute its motion toward a goal position according to range information about environments, acquired by ultrasonic sensors, without the need for trajectory planning. A global planner, therefore, only needs to generate some subgoal positions rather than exact geometric paths. Because such subgoals can be easily removed from or added into the planner, this strategy reduces computational time for global planning and is flexible for replanning in dynamic environments. Simulation results demonstrate that the proposed strategy can be applied to robot motion in complex and dynamic environments.