# Resilience by Reconfiguration: Exploiting Heterogeneity in Robot Teams

## TL;DR

This paper introduces a reconfiguration method for heterogeneous robot teams to maintain resource availability after failures, optimizing communication and formation using integer semi-definite programming and simulated annealing.

## Contribution

It presents a novel approach combining integer semi-definite programming and simulated annealing for rapid reconfiguration of robot teams after resource failures.

## Key findings

- Method achieves reconfiguration in a few seconds.
- Validated in simulation and real-robot experiments.
- Maintains high resource availability despite failures.

## Abstract

We propose a method to maintain high resource in a networked heterogeneous multi-robot system to resource failures. In our model, resources such as and computation are available on robots. The robots engaged in a joint task using these pooled resources. In our model, a resource on a particular robot becomes unavailable e.g., a sensor ceases to function due to a failure), the system reconfigures so that the robot continues to have to this resource by communicating with other robots. Specifically, we consider the problem of selecting edges to be in the system's communication graph after a resource has occurred. We define a metric that allows us to characterize the quality of the resource distribution in the represented by the communication graph. Upon a resource becoming unavailable due to failure, we reconfigure network so that the resource distribution is brought as to the ideal resource distribution as possible without a big change in the communication cost. Our approach uses integer semi-definite programming to achieve this goal. We also provide a simulated annealing method to compute a formation that satisfies the inter-robot distances imposed by the topology, along with other constraints. Our method can compute a communication topology, spatial formation, and formation change motion planning in a few seconds. We validate our method in simulation and real-robot experiments with a team of seven quadrotors.