Revising motion planning under Linear Temporal Logic specifications in partially known workspaces

Abstract

In this paper we propose a generic framework for real-time motion planning based on model-checking and revision. The task specification is given as a Linear Temporal Logic formula over a finite abstraction of the robot motion. A preliminary motion plan is first generated based on the initial knowledge of the system model. Then real-time information obtained during the runtime is used to update the system model, verify and further revise the motion plan. The implementation and revision of the motion plan are performed in real-time. This framework can be applied to partially-known workspaces and workspaces with large uncertainties. Computer simulations are presented to demonstrate the efficiency of the framework.