T* : A Heuristic Search Based Algorithm for Motion Planning with Temporal Goals

TL;DR

This paper introduces T*, a heuristic search algorithm that efficiently generates optimal trajectories for robots with complex temporal goals, significantly outperforming existing methods in solving temporal logic motion planning problems.

Contribution

The paper presents T*, a novel A*-based algorithm that opportunistically solves temporal logic motion planning problems, improving efficiency over current state-of-the-art approaches.

Findings

T* achieves an order of magnitude faster performance.

T* successfully generates optimal trajectories for complex temporal goals.

Experimental results validate T*’s superior efficiency over existing algorithms.

Abstract

Motion planning is the core problem to solve for developing any application involving an autonomous mobile robot. The fundamental motion planning problem involves generating a trajectory for a robot for point-to-point navigation while avoiding obstacles. Heuristic-based search algorithms like A* have been shown to be extremely efficient in solving such planning problems. Recently, there has been an increased interest in specifying complex motion plans using temporal logic. In the state-of-the-art algorithm, the temporal logic motion planning problem is reduced to a graph search problem and Dijkstra's shortest path algorithm is used to compute the optimal trajectory satisfying the specification. The A* algorithm when used with a proper heuristic for the distance from the destination can generate an optimal path in a graph efficiently. The primary challenge for using A* algorithm in temporal logic path planning is that there is no notion of a single destination state for the robot. In this thesis, we present a novel motion planning algorithm T* that uses the A* search procedure in temporal logic path planning \emph{opportunistically} to generate an optimal trajectory satisfying a temporal logic query. Our experimental results demonstrate that T* achieves an order of magnitude improvement over the state-of-the-art algorithm to solve many temporal logic motion planning problems.