1. Students: Yossi Turgeman Avi Deri Self-Stabilizing and Efficient Robust Uncertainty Management Instructor: Prof Michel Segal

2. Background And Motivation Autonomous Flocking Behavior Efficient Ad-Hoc Network

3. Introduction  Unmanned Aerial Vehicle (AKA UAV), is a remotely piloted aircraft  Traditionally UAVs come in two varieties:  Controlled from a remote location.  Fly autonomously based on pre programmed flight plans Background And Motivation

4. Autonomous Behavior  Usually UAV’s used pre-flight programmed flight path, or controlled by a human operator. while those are simple methods they are not optimal.  We try to give a UAV a set of behavioral rules which will allow it to fly autonomously, and without a predefined flight path. Background And Motivation

5. Project Goals The project has two main goals:  Establishing a robust and self stabilizing algorithm that would imitate the way birds fly in real life.  Minimizing the total power consumption by heavy duty messages such as backups by using a unique hop bounded graph algorithm. Background And Motivation

6. Chosen Approach  In this project we implement an algorithm to define behaviors for a team of autonomous agents. In order to maintain such a group of autonomous UAVs (a swarm) we will establish a set of behavioral rules of flying as a group.  The hop bounded graph will be constructed using a specially designed algorithm by prof Micheal Segal. Background And Motivation

7. Background And Motivation Autonomous Flocking Behavior Efficient Ad-Hoc Network

8. Autonomous Flocking Behavior  In order to achieve full autonomous self stabilizing automata we needed a strong stable and efficient algorithm which will able a UAV to move around in solo and in group mode.  This algorithm will try to imitate a real life birds flocking with no leader election. Behavioral Algorithm

9. Autonomous Flocking Behavior  "Flocks, Herds, and Schools A Distributed Behavioral Model” or "Boids", developed by Craig Reynolds in 1986, is an artificial life algorithm, simulating the flocking behavior of birds.  This algorithm is commonly used in the animation industry. Chosen Algorithm – “Boids”

10. Autonomous Flocking Behavior  Each UAV has a detection range and a separation range:  The detection range - The distance at which UAV's can detect other UAVs.  The separation range - The distance at which a UAV might steer to avoid other UAVs.  Until something falls within a UAV's detection range, it will not react to it. A Few Definitions

11. Autonomous Flocking Behavior The algorithm consists of 3 rules: Separation Alignment Cohesion The 3 Rules

12. Autonomous Flocking Behavior UAV's are repelled by other UAV's. When another UAV come within a cretin critical range, the UAV will turn to avoid other UAVs which get too close. Separation

13. Autonomous Flocking Behavior The UAV tends to turn so that it is moving in the same direction that nearby UAVs are moving. Alignment

14. Autonomous Flocking Behavior UAVs within a flock are attracted to each other as long as they are within the detection range, but outside the separation range. This goal is to have the UAVs flock together, but not to be so close that they are on top of each other. Cohesion

15. Autonomous Flocking Behavior While the “boids” algorithm is wildly used (in the animation industry) it is not perfect.Our contribution comes in the form of 2 additional rules that will not allow the following symmetry scenarios : Symmetry breaking

16. Autonomous Flocking Behavior Flocking demonstration – Single UAV

17. Autonomous Flocking Behavior Flocking demonstration – 4 UAVs

18. Autonomous Flocking Behavior Flocking demonstration – 15 UAVs

19. Background And Motivation Autonomous Flocking Behavior Efficient Ad-Hoc Network

20. Efficient Ad-Hoc Network  The network lifetime is defined as the time until the first member of the network consumes his battery and can not communicate anymore.  We would like to maximize this time by creating an efficient as possible network. Efficient Algorithm

21. Efficient Ad-Hoc Network  Given a collected information through messages concerning the position, speed and direction of other members, the members will form an ad-hoc network by deciding on individual transmission ranges.  The resulting network has important topological properties such as strong connectivity and small diameter (Hop bounded). Efficient Algorithm Contd.

22. Efficient Ad-Hoc Network  Within a given interval [Ts,Tf], we construct the network by performing this actions:  Calculate all possible radii and distance functions between all two nodes.  Form all the possible h bounded graphs that we can create, we choose the one that will give us the longest network life as previously defined. Efficient Algorithm Contd.

23. Efficient Ad-Hoc Network  The result is a strongly connected h-hop bounded graph which is the same in every UAV although it was calculated in a distributed manner.  Using this graph as the backbone for heavy duty massages will prolong the network life. Efficient Algorithm Contd.

24. The End Any questions ? Thank You