TECHLAV Annual Meeting Greensboro, NC May 31-June 1, 2017 Testing, Evaluation, and Control of Heterogeneous Large-Scale Systems of Autonomous Vehicles (TECHLAV) TECHLAV Annual Meeting Greensboro, NC May 31-June 1, 2017 http://techlav.ncat.edu/

Task Allocation Using Parallelized Clustering and Auctioning Algorithms for Heterogeneous Robotic Swarms Operating on a Cloud Network By: Jonathan Lwowski, Dr. Patrick Benavidez, Dr. Jeff Prevost, and Dr. Mo Jamshidi

Overview Introduction System Overview Clustering Auctioning Genetic Algorithm Hardware Emulation Results Conclusion

Motivation Costa Concordia cruise ship crash 45250 passengers 33 deaths and 64 injuries Goals of Task T1-6 (Cloud-Based Control of LSASV) and Sub-thrust 1-2 (Cooperative Localization, Navigation and Control of LSASV) Heterogenous swarm robotics Task allocation Navigation/Path Planning Autonomous Cloud-based robotics Data simplification and organization

Significance of Methods One of the first heterogenous swarms of MAVs and ASVs to use a cloud-based network to simplify and organize data to be used for task allocation Parallelization of data simplification algorithms on the cloud to increase speed and scalability Tested in both software simulations and hardware emulations

System Overview

Localizing the People MAVs equipped with bottom facing stereo cameras People represented by colored circles in simulation People detected by simple color thresholding techniques People localized using stereo camera model and coordinate transformations

Clustering the People Used to simplify the map Group people into clusters of a given radius Constrained Cluster Radius K-mean Clustering Parallelized CCR K-means Clustering on the cloud

Clustering the People 500 people and a desired cluster radius of 10 meters

Parallelization of Clustering Algorithm

Meta-clustering the Clusters Groups the clusters into meta-clusters Number of meta-clusters same as the number of ASVs Fuzzy C-means Clustering Allows clusters to be assigned to multiple meta-clusters

Meta-clustering the Clusters 500 people, desired cluster radius of 10 meters, 5 ASVs

Auctioning the Meta-clusters Used to assign a meta-cluster to each ASV Uses ASVs locations, capacities, and speeds Uses meta-clusters locations and number of people in the meta-cluster Uses mothership location

Auctioning the Meta-clusters

Traveling to Assigned Clusters Genetic algorithm Fitness value = 1/distance 50 random permutations chosen as initial population

Hardware Emulation Results GPS emulation Overhead webcam Ar-track-alvar Provides x,y, and z position of each Ar-Tag Used to emulate the GPS and stereo camera localization of the MAVs

Hardware Emulation Results GPS Emulation

Hardware Emulation Results CCR K-mean Clustering 10 Ar-Tags representing people and 4 Clusters

Hardware Emulation Results CCR K-mean Clustering 10 Ar-Tags representing people and 4 Clusters

Hardware Emulation Results Video https://www.youtube.com/watch?v=9oAiLII6xr8

Conclusion One of the first heterogenous swarms of MAVs and ASVs to use a cloud based network to simplify and organize the data to be used for task allocation Parallelization of data simplification algorithms to increase speed and scalability of the system Effectiveness of systems demonstrated in software and hardware experiments

Future Work Use deep learning to detect and localize the people Add autonomous underwater robots to detect people under the surface of the water During the summer, Dr. Joordens will implement this using model boats on an actual body of water

References J. Lwowski, P. Benavidez, J. J. Prevost, and M. Jamshidi, "Task Allocation Using Parallelized Clustering and Auctioning Algorithms for Heterogeneous Robotic Swarms Operating on a Cloud Network,” Chapter 3 in Autonomy and Artificial Intelligence, (W. F. Lawless, et al, eds.), Springer-Verlag, 2017 (In Press)