Two-Phase Motion Planning Under Signal Temporal Logic Specifications in Partially Unknown Environments

Abstract

This article studies the planning problem for a robot residing in partially unknown environments under signal temporal logic (STL) specifications, where most of the existing planning methods using STL rely on a fully known environment. In many practical scenarios, however, robots do not have prior information of all the obstacles. In this article, a novel two-phase planning method, i.e., offline exploration followed by online planning, is proposed to efficiently synthesize paths that satisfy STL tasks. In the offline exploration phase, a rapidly exploring random tree* (RRT*) is grown from task regions under the guidance of timed transducers, which guarantees that the resultant paths satisfy the task specifications. In the online phase, the path with minimum cost in RRT* is determined when an initial configuration is assigned. This path is then set as the reference of the time elastic band algorithm, which modifies the path until it has no collisions with obstacles. It is shown that the online computational burden is reduced, and collisions with unknown obstacles are avoided by using the proposed planning framework. The effectiveness and superiority of the proposed method are demonstrated in simulations and real-world experiments.