# On multi-robot search for a stationary object

## TL;DR

This paper introduces new heuristic algorithms for multi-robot search problems in known environments, improving solution quality and computational efficiency over standard clustering methods through a cluster-first route-second approach.

## Contribution

It proposes novel heuristics for multi-robot search variants, generalizing classic problems, and demonstrates their effectiveness through computational experiments.

## Key findings

- Heuristics outperform k-means clustering in solution quality and speed.
- Integrated approach improves solutions by up to 15%.
- Heuristics are effective for large benchmark instances.

## Abstract

Two variants of multi-robot search for a stationary object in a priori known environment represented by a graph are studied in the paper. The first one is a generalization of the Traveling Deliveryman Problem where more than one deliveryman is allowed to be used in a solution. Similarly, the second variant is a generalization of the Graph Search Problem. A novel heuristics suitable for both problems is proposed which is furthermore integrated into a cluster-first route second approach. A set of computational experiments was conducted over the benchmark instances derived from the TSPLIB library. The results obtained show that even a standalone heuristics significantly outperforms the standard solution based on k- means clustering in quality of results as well as computational time. The integrated approach furthermore improves solutions found by a standalone heuristics by up to 15% at the expense of higher computational complexity.