The Team Surviving Orienteers Problem: Routing Robots in Uncertain Environments with Survival Constraints

TL;DR

This paper introduces the Team Surviving Orienteers (TSO) problem, optimizing multi-robot paths in uncertain environments to maximize node coverage while ensuring survival probabilities, with an efficient approximation algorithm validated through simulations.

Contribution

The paper formalizes the TSO problem, proposes a greedy approximation algorithm with provable bounds, and demonstrates its scalability and near-optimal performance in simulations.

Findings

The proposed greedy approach achieves solutions within a bounded factor of the optimum.

The algorithm scales linearly with team size and polynomially with graph size.

Simulations show the method performs close to optimal on large problem instances.

Abstract

In this paper we study the following multi-robot coordination problem: given a graph, where each edge is weighted by the probability of surviving while traversing it, find a set of paths for $K$ robots that maximizes the expected number of nodes collectively visited, subject to constraints on the probability that each robot survives to its destination. We call this problem the Team Surviving Orienteers (TSO) problem. The TSO problem is motivated by scenarios where a team of robots must traverse a dangerous, uncertain environment, such as aid delivery in disaster or war zones. We present the TSO problem formally along with several variants, which represent "survivability-aware" counterparts for a wide range of multi-robot coordination problems such as vehicle routing, patrolling, and informative path planning. We propose an approximate greedy approach for selecting paths, and prove that the value of its output is bounded within a factor $1-e^{-p_s/\lambda}$ of the optimum where $p_s$ is the per-robot survival probability threshold, and $1/\lambda \le 1$ is the approximation factor of an oracle routine for the well-known orienteering problem. Our approach has linear time complexity in the team size and polynomial complexity in the graph size. Using numerical simulations, we verify that our approach is close to the optimum in practice and that it scales to problems with hundreds of nodes and tens of robots.