Task Allocation Using Parallelized Clustering and Auctioning Algorithms for Heterogeneous Robotic Swarms Operating on a Cloud Network

TL;DR: A novel centralized robotic swarm of heterogeneous unmanned vehicles consisting of autonomous surface vehicles and micro-aerial vehicles is presented and Manipulations of the swarm demonstrate how aspects of individual robotic platforms can be controlled cooperatively to accomplish a group task in an efficient manner.

Abstract: In this paper, a novel centralized robotic swarm of heterogeneous unmanned vehicles consisting of autonomous surface vehicles and micro-aerial vehicles is presented. The swarm robots operate in an outdoor environment and are equipped with cameras and Global Positioning Systems (GPS). Manipulations of the swarm demonstrate how aspects of individual robotic platforms can be controlled cooperatively to accomplish a group task in an efficient manner. We demonstrate the use of air-based robots to build a map of important features of the local environment, such as the locations of targets. The map is then sent to a cloud-based cluster on a remote network. The cloud performs clustering algorithms using the map to calculate optimal clusters of the targets. The cloud then performs an auctioning algorithm to assign the clusters to the surface-based robots based on several factors such as relative position and capacities. The surface-robots then travel to their assigned clusters to complete the allocated tasks. Lastly, we present the results of simulating our cooperative swarm in both software and hardware, demonstrating the effectiveness of our proposed algorithm.