An LTL-Based Motion and Action Dynamic Planning Method for Autonomous Robot*

LTL-based languages provide a formal and expressive way to specify high-level complex motion and action tasks for autonomous robots. Based on LTL task specification, a method to dynamically generate motion and action sequences for a robot is proposed. Firstly, the environment knowledge, robot’s motion and action capabilities are described respectively based on the partitioned regions. Secondly, a weighted finite transition system is constructed from these models to capture the robot’s full functionality and the knowledge of environment. Thirdly, a dynamic planning framework is put forward considering environment update or operations failure. Finally, a fire-fighting case is simulated to demonstrate the effectiveness of the approach.