Team Orienteering Coverage Planning with Uncertain Reward

TL;DR

This paper introduces a novel approach for multi-robot area coverage planning under uncertain, location-dependent costs, combining estimation and optimization to improve routing efficiency in real-world scenarios.

Contribution

It formulates the TOCPUR problem, proposes a mixed integer programming solution for the TOCP, and demonstrates superior performance over existing methods and a real-world robot deployment.

Findings

Proposed a new mixed integer programming formulation for TOCP.

Outperforms greedy algorithms and exact TOP solutions in benchmarks.

Successfully demonstrated on a team of physical robots in real environment.

Abstract

Many municipalities and large organizations have fleets of vehicles that need to be coordinated for tasks such as garbage collection or infrastructure inspection. Motivated by this need, this paper focuses on the common subproblem in which a team of vehicles needs to plan coordinated routes to patrol an area over iterations while minimizing temporally and spatially dependent costs. In particular, at a specific location (e.g., a vertex on a graph), we assume the cost grows linearly in expectation with an unknown rate, and the cost is reset to zero whenever any vehicle visits the vertex (representing the robot servicing the vertex). We formulate this problem in graph terminology and call it Team Orienteering Coverage Planning with Uncertain Reward (TOCPUR). We propose to solve TOCPUR by simultaneously estimating the accumulated cost at every vertex on the graph and solving a novel variant of the Team Orienteering Problem (TOP) iteratively, which we call the Team Orienteering Coverage Problem (TOCP). We provide the first mixed integer programming formulation for the TOCP, as a significant adaptation of the original TOP. We introduce a new benchmark consisting of hundreds of randomly generated graphs for comparing different methods. We show the proposed solution outperforms both the exact TOP solution and a greedy algorithm. In addition, we provide a demo of our method on a team of three physical robots in a real-world environment.