SAPFANET: Spatially Adaptive Positioning for FANETsJoshua Rentrope and Dr. Mustafa Ilhan AkbasComputer ScienceFlorida Polytechnic University

Design and Analysis of Intelligent Vehicular Networks and Applications (DIVANET)November 21, 2017

Table of Contents

1. Introduction2. Problem Definition3. Previous System4. Optimizations5. Simulation Results6. Conclusions

Introduction

● UAV and UAV Swarm technologies have advanced● Increased capabilities for UAVs

○ Better optical and auditory sensors○ More reliable communications○ Flexible controller interfaces

● Current research is effective swarm applications and autonomy

Problem Requirements

● UAV Swarm that can fill irregular environments (Odd spatial domains, Obstacles in environment)

● A formulation that can adjust UAV behavior to its environment● Applications of this protocol to hazardous tasks (search and

rescue, surveillance)

Problem Specification

● Already existing 3d Swarms do not give enough weight to the environmental constraints

● There may need to be a centralized system

● Objective○ Create optimizations that consider the environment and can dynamically

reposition UAVs into better configurations

Previous Systems

● Virtual Forces○ Adjusts drone positions based on nearby drones and their relations○ Good in theory, but it is expensive in terms of communication○ This approach also ignores environmental macro-behaviours

● VSEPR Drone Positioning○ Calculates the equilibrium for Virtual Forces based on the geometry of the atoms○ Optimizes the communication needed by the swarm by updating key positions

and pair numbers

VSEPR Positioning

● VSEPR provides the optimal positions from Virtual Forces

● Can be a hierarchical organization and decentralized from a main controller

● VSEPR does not take into account the environment. Less optimal as Environment changes

APAWSAN: VSEPR-based Positioning

Actor positioning for aerial wireless sensor and actor networks

● APAWSAN uses precalculated VSEPR positions for drone configurations● A leader drone will broadcast its position to the follower drones● Each follower drone will move to a its labeled position based on the formula

This works great for unbounded spaces, each drone will have optimal angles. However, this solution is not generalized for corridor-like spaces and with obstacles

SAPFANET: An Optimization

● Spatially Adaptive Positioning for FANETs merges the spatial constraints of the environment to generate drone positions

● SAPFANET assigns spatial sub-domains to drone positions and attempts to equalize them as much as possible.

● First it calculates the optimal subdomains.○ Each subdomain is defined by a set

boundary planes. ○ Some boundary planes are the

constraining planes of the environment○ Boundary planes we can manipulate are

planes that part between two drones○ A configuration, which keeps each volume

of the set as close to the average as possible, is estimated

SAPFANET

SAPFANET Results

When compared to APAWSAN, the pure VSEPR configuration, SAPFANET displayed favorable results in terms of space

The time of convergence was slightly higher, but can be reduced through better communication protocols

Conclusion

● Can adapt VSEPR to dynamically constrained environments● SAPFANET is mainly suited for static environment, and needs further adaptation

for high mobility systems● Future work

○ More tests with obstacles, discontinuities in environment○ Using more realistic antenna models for communication○ Currently designing a simulation for Search and Rescue using SAPFANET

configurations and belief networks