The Team Surviving Orienteers Problem: Routing Robots in Uncertain Environments with Survival Constraints
This addresses multi-robot coordination in dangerous scenarios like disaster zones, but is incremental as it adapts existing orienteering methods with survival constraints.
The paper tackles the Team Surviving Orienteers problem, which involves routing multiple robots in uncertain environments to maximize expected visited nodes while ensuring each robot meets a survival probability threshold, and proposes an approximate greedy approach with a proven performance bound and linear time complexity in team size.
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 $1-e^{-p_s/λ}$ of the optimum where $p_s$ is the per-robot survival probability threshold, and $1/λ\le 1$ 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.