Simultaneous Human-robot Matching and Routing for Multi-robot Tour Guiding under Time Uncertainty

TL;DR

This paper introduces a scalable multi-robot tour guidance framework that efficiently matches humans to robots and plans routes under environmental uncertainty, demonstrated through computational and simulation evaluations.

Contribution

It formulates a simultaneous matching and routing problem for multi-robot tour guiding under uncertainty and develops a large neighborhood search algorithm for efficient solutions.

Findings

The planner scales to 50 robots, 250 humans, and 50 POIs.

The approach maintains robustness despite uncertainty in environment estimations.

Simulation results confirm effective tour guiding in uncertain indoor environments.

Abstract

This work presents a framework for multi-robot tour guidance in a partially known environment with uncertainty, such as a museum. In the proposed centralized multi-robot planner, a simultaneous matching and routing problem (SMRP) is formulated to match the humans with robot guides according to their selected places of interest (POIs) and generate the routes and schedules for the robots according to uncertain spatial and time estimation. A large neighborhood search algorithm is developed to efficiently find sub-optimal low-cost solutions for the SMRP. The scalability and optimality of the multi-robot planner are evaluated computationally under different numbers of humans, robots, and POIs. The largest case tested involves 50 robots, 250 humans, and 50 POIs. Then, a photo-realistic multi-robot simulation platform was developed based on Habitat-AI to verify the tour guiding performance in an uncertain indoor environment. Results demonstrate that the proposed centralized tour planner is scalable, makes a smooth trade-off in the plans under different environmental constraints, and can lead to robust performance with inaccurate uncertainty estimations (within a certain margin).