Optimal path planning for surveillance with temporal-logic constraints

Abstract

In this paper we present a method for automatically generating optimal robot paths satisfying high-level mission specifications. The motion of the robot in the environment is modeled as a weighted transition system. The mission is specified by an arbitrary linear temporal-logic (LTL) formula over propositions satisfied at the regions of a partitioned environment. The mission specification contains an optimizing proposition, which must be repeatedly satisfied. The cost function that we seek to minimize is the maximum time between satisfying instances of the optimizing proposition. For every environment model, and for every formula, our method computes a robot path that minimizes the cost function. The problem is motivated by applications in robotic monitoring and data-gathering. In this setting, the optimizing proposition is satisfied at all locations where data can be uploaded, and the LTL formula specifies a complex data-collection mission. Our method utilizes Büchi automata to produce an automaton (which can be thought of as a graph) whose runs satisfy the temporal-logic specification. We then present a graph algorithm that computes a run corresponding to the optimal robot path. We present an implementation for a robot performing data collection in a road-network platform.This material is based upon work supported in part by ONR-MURI (award N00014-09-1-1051), ARO (award W911NF-09-1-0088), and Masaryk University (grant numbers LH11065 and GD102/09/H042), and other funding sources (AFOSR YIP FA9550-09-1-0209, NSF CNS-1035588, NSF CNS-0834260). (N00014-09-1-1051 - ONR-MURI; W911NF-09-1-0088 - ARO; LH11065 - Masaryk University; GD102/09/H042 - Masaryk University; FA9550-09-1-0209 - AFOSR YIP; CNS-1035588 - NSF; CNS-0834260 - NSF