Optimal multi-robot path planning with temporal logic constraints

In this paper we present a method for automatically planning optimal paths for a group of robots that satisfy a common high level mission specification. Each robot's motion in the environment is modeled as a weighted transition system. The mission is given as a Linear Temporal Logic formula. In addition, an optimizing proposition must repeatedly be satisfied. The goal is to minimize the maximum time between satisfying instances of the optimizing proposition. Our method is guaranteed to compute an optimal set of robot paths. We utilize a timed automaton representation in order to capture the relative position of the robots in the environment. We then obtain a bisimulation of this timed automaton as a finite transition system that captures the joint behavior of the robots and apply our earlier algorithm for the single robot case to optimize the group motion. We present a simulation of a persistent monitoring task in a road network environment.United States. Office of Naval Research. Multidisciplinary University Research Initiative (N00014-09-1051)United States. Army Research Office (W911NF-09-1-0088)United States. Air Force Office of Scientific Research (YIP FA9550-09-1-020)National Science Foundation (U.S.). (CNS- 0834260

Optimality and robustness in multi-robot path planning with temporal logic constraints

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 work was supported in part by the Office of Naval Research (grant number MURI N00014-09-1051), Army Research Office (grant number W911NF-09-1-0088), Air Force Office of Scientific Research (grant number YIP FA9550-09-1-020), National Science Foundation (grant number CNS-0834260), Singapore-MIT Alliance for Research and Technology (SMART) Future of Urban Mobility Project and by Natural Sciences and Engineering Research Council of Canada. (MURI N00014-09-1051 - Office of Naval Research; W911NF-09-1-0088 - Army Research Office; YIP FA9550-09-1-020 - Air Force Office of Scientific Research; CNS-0834260 - National Science Foundation; Singapore-MIT Alliance for Research and Technology (SMART); Natural Sciences and Engineering Research Council of Canada

Technical Report: A Receding Horizon Algorithm for Informative Path Planning with Temporal Logic Constraints

This technical report is an extended version of the paper 'A Receding Horizon Algorithm for Informative Path Planning with Temporal Logic Constraints' accepted to the 2013 IEEE International Conference on Robotics and Automation (ICRA). This paper considers the problem of finding the most informative path for a sensing robot under temporal logic constraints, a richer set of constraints than have previously been considered in information gathering. An algorithm for informative path planning is presented that leverages tools from information theory and formal control synthesis, and is proven to give a path that satisfies the given temporal logic constraints. The algorithm uses a receding horizon approach in order to provide a reactive, on-line solution while mitigating computational complexity. Statistics compiled from multiple simulation studies indicate that this algorithm performs better than a baseline exhaustive search approach.Comment: Extended version of paper accepted to 2013 IEEE International Conference on Robotics and Automation (ICRA