Unreliable Sensors for Reliable Efficient Robots

TL;DR

This paper investigates the impact of sensor unreliability on mobile robot swarm algorithms, introducing new algorithms and benchmarking their performance in terms of fuel efficiency and reliability under various conditions.

Contribution

It presents a simulation framework for benchmarking robot algorithms and introduces two new algorithms that operate reliably despite sensor errors.

Findings

Sensor unreliability affects rendezvous and leader election protocols.

New algorithms achieve reliable convergence and leader election with sensor errors.

Benchmarking shows trade-offs between fuel efficiency and reliability.

Abstract

The vast majority of existing Distributed Computing literature about mobile robotic swarms considers computability issues: characterizing the set of system hypotheses that enables problem solvability. By contrast, the focus of this work is to investigate complexity issues: obtaining quantitative results about a given problem that admits solutions. Our quantitative measurements rely on a newly developed simulation framework to benchmark pen and paper designs. First, we consider the maximum traveled distance when gathering robots at a given location, not known beforehand (both in the two robots and in the n robots settings) in the classical OBLOT model, for the FSYNC, SSYNC, and ASYNC schedulers. This particular metric appears relevant as it correlates closely to what would be real world fuel consumption. Then, we introduce the possibility of errors in the vision of robots, and assess the behavior of known rendezvous (aka two robots gathering) and leader election protocols when sensors are unreliable. We also introduce two new algorithms, one for fuel efficient convergence, and one for leader election, that operate reliably despite unreliable sensors.