The document discusses the optimization of UAV (drone) swarms for intruder detection using the CACO model and genetic algorithms. It presents a comprehensive introduction to UAV applications, the model's parameters, results from optimization, and future enhancements planned for inter-swarm collaborations. The research indicates improved detection rates and plans to expand the model's use across different scenarios.

1. University of Luxembourg
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Optimizing the Performance of an Unpredictable
UAV Swarm for Intruder Detection
Daniel H. Stolfi1 Matthias R. Brust1 Grégoire Danoy1,2 Pascal Bouvry1,2
OLA’2020 – International Conference on Optimization and Learning
February 17
th
-19
th
, 2020
1
SnT - Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Luxembourg
2
FSTM/DCS, University of Luxembourg, Luxembourg

2. CONTENTS
1 INTRODUCTION
2 CACOC MOBILITY MODEL
3 PARAMETER OPTIMIZATION
4 RESULTS
5 CONCLUSION AND FUTURE WORK
1/13

3. CONTENTS
1 INTRODUCTION
2 CACOC MOBILITY MODEL
3 PARAMETER OPTIMIZATION
4 RESULTS
5 CONCLUSION AND FUTURE WORK
1/13

4. CONTENTS
1 INTRODUCTION
2 CACOC MOBILITY MODEL
3 PARAMETER OPTIMIZATION
4 RESULTS
5 CONCLUSION AND FUTURE WORK
1/13

5. CONTENTS
1 INTRODUCTION
2 CACOC MOBILITY MODEL
3 PARAMETER OPTIMIZATION
4 RESULTS
5 CONCLUSION AND FUTURE WORK
1/13

6. CONTENTS
1 INTRODUCTION
2 CACOC MOBILITY MODEL
3 PARAMETER OPTIMIZATION
4 RESULTS
5 CONCLUSION AND FUTURE WORK
1/13

7. INTRODUCTION
UAVs (drones) has gained importance in recent years.
Environmental monitoring
Early wildfire detection
Architecture surveillance
Goods transportation
Cooperative surveillance
2/13

8. INTRODUCTION
UAVs (drones) has gained importance in recent years.
Environmental monitoring
Early wildfire detection
Architecture surveillance
Goods transportation
Cooperative surveillance
2/13

9. INTRODUCTION
UAVs (drones) has gained importance in recent years.
Environmental monitoring
Early wildfire detection
Architecture surveillance
Goods transportation
Cooperative surveillance
2/13

10. INTRODUCTION
UAVs (drones) has gained importance in recent years.
Environmental monitoring
Early wildfire detection
Architecture surveillance
Goods transportation
Cooperative surveillance
2/13

11. INTRODUCTION
UAVs (drones) has gained importance in recent years.
Environmental monitoring
Early wildfire detection
Architecture surveillance
Goods transportation
Cooperative surveillance
2/13

12. INTRODUCTION
UAVs (drones) has gained importance in recent years.
Environmental monitoring
Early wildfire detection
Architecture surveillance
Goods transportation
Cooperative surveillance
2/13

13. MOTIVATION
Mobility model for surveillance tasks.
Swarm of UAVs (and UGVs, etc.)
Improve coverage
Unpredictable trajectories
Deterministic routes (for the Ground Control Station)
Bioinspired Predator-Prey approach
3/13

14. MOTIVATION
Mobility model for surveillance tasks.
Swarm of UAVs (and UGVs, etc.)
Improve coverage
Unpredictable trajectories
Deterministic routes (for the Ground Control Station)
Bioinspired Predator-Prey approach
3/13

15. PHEROMONE MODEL (PREDATORS)
4/13

16. CACOC MOBILITY MODEL1
Chaotic attractor B solution of the Rössler system
1M. Rosalie, G. Danoy, S. Chaumette, and P. Bouvry. “Chaos-enhanced mobility models for multilevel swarms of UAVs”. In: Swarm and
Evolutionary Computation 41.November 2017 (2018), pp. 36–48.
5/13

17. CACOC MOBILITY MODEL1
Chaotic attractor B solution of the Rössler system
TABLE: Parameters proposed for CACOC.
Parameter Units Range
Pheromone amount (τa) % [1 − 100]
Pheromone radius (τr ) cells [0.5 − 2.5]
Pheromone scan depth (τd ) cells [1 − 10]
1M. Rosalie, G. Danoy, S. Chaumette, and P. Bouvry. “Chaos-enhanced mobility models for multilevel swarms of UAVs”. In: Swarm and
Evolutionary Computation 41.November 2017 (2018), pp. 36–48.
5/13

18. OPTIMIZATION ALGORITHMS: GA
Genetic Algorithm (GA)
6/13

19. OPTIMIZATION ALGORITHMS: GA
Genetic Algorithm (GA)
6/13

20. OPTIMIZATION ALGORITHMS: GA
Genetic Algorithm (GA)
Fitness Function
F(x) =
1
γ i
# of intrudersi − # of intruders at destinationi
# of intrudersi
, γ = 10 scenarios
(maximisation of the UAVs’ sucess rate)
6/13

21. OPTIMIZATION ALGORITHMS: CCGA-2
Cooperative Coevolutionary Genetic Algorithm (CCGA-2)2
2M. A. Potter and K. A. De Jong. “A cooperative coevolutionary approach to function optimization”. In: Parallel Problem Solving from
Nature — PPSN III. ed. by Y. Davidor, H.-P. Schwefel, and R. Männer. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994, pp. 249–257.
7/13

22. INTRUDER MOBILITY MODEL (PREYS)
Attractive and repulsive forces
8/13

23. CASE STUDIES
TABLE: Characteristics of our 12 case studies.
Case Study # Scenarios # UAVs # Intruders
4.2 10 4 2
8.2 10 8 2
8.4 10 8 4
8.6 10 8 6
12.2 10 12 2
12.4 10 12 4
12.6 10 12 6
12.8 10 12 8
16.2 10 16 2
16.4 10 16 4
16.6 10 16 6
16.8 10 16 8
9/13

24. RESULTS (OPTIMIZATION)
TABLE: Results of the optimisation process performed by CCGA-2 (30 runs).
Case Study
Fitness Time
(Hours)Avg. StdDev. Min. Max.
4.2 0.710 0.064 0.550 0.850 1.0
8.2 0.905 0.038 0.850 1.000 2.9
8.4 0.808 0.031 0.750 0.850 3.1
8.6 0.775 0.035 0.717 0.850 3.0
12.2 0.982 0.025 0.950 1.000 6.1
12.4 0.926 0.021 0.900 0.975 6.1
12.6 0.888 0.023 0.850 0.967 6.0
12.8 0.863 0.018 0.825 0.900 6.1
16.2 1.000 0.000 1.000 1.000 10.2
16.4 0.983 0.012 0.975 1.000 10.4
16.6 0.960 0.016 0.933 1.000 10.5
16.8 0.945 0.012 0.925 0.963 10.4
10/13

25. RESULTS (OPTIMIZATION)
TABLE: Results of the optimisation process performed by CCGA-2 (30 runs).
Case Study
Fitness Time
(Hours)Avg. StdDev. Min. Max.
4.2 0.710 0.064 0.550 0.850 1.0
8.2 0.905 0.038 0.850 1.000 2.9
8.4 0.808 0.031 0.750 0.850 3.1
8.6 0.775 0.035 0.717 0.850 3.0
12.2 0.982 0.025 0.950 1.000 6.1
12.4 0.926 0.021 0.900 0.975 6.1
12.6 0.888 0.023 0.850 0.967 6.0
12.8 0.863 0.018 0.825 0.900 6.1
16.2 1.000 0.000 1.000 1.000 10.2
16.4 0.983 0.012 0.975 1.000 10.4
16.6 0.960 0.016 0.933 1.000 10.5
16.8 0.945 0.012 0.925 0.963 10.4
10/13

26. RESULTS (TESTING)
What happens if the intruders have a different behaviour?
11/13

27. RESULTS (TESTING)
TABLE: Average success rate obtained in 100 unseen scenarios of each case study.
UAVs
Intruders
2 4 6 8 Mean
4 16.0% — — — 16.0%
8 57.5% 57.3% 59.0% — 58.2%
12 79.5% 75.8% 74.3% 76.0% 75.8%
16 86.0% 87.0% 84.0% 85.1% 85.3%
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28. RESULTS (TESTING)
TABLE: Average success rate obtained in 100 unseen scenarios of each case study.
UAVs
Intruders
2 4 6 8 Mean
4 16.0% — — — 16.0%
8 57.5% 57.3% 59.0% — 58.2%
12 79.5% 75.8% 74.3% 76.0% 75.8%
16 86.0% 87.0% 84.0% 85.1% 85.3%
11/13

29. SURVEILLANCE EXAMPLE
12/13

30. CONCLUSION AND FUTURE WORK
We have. . .
. . . added new features to CACOC (parameters)
. . . optimised these parameters using CCGA-2
. . . improved the intruder detection rate
We plan to. . .
. . . use CACOC in other different scenarios
. . . implement inter-swarm collaborations (different vehicles)
. . . improve this P-P approach using a competitive algorithm
13/13

31. CONCLUSION AND FUTURE WORK
We have. . .
. . . added new features to CACOC (parameters)
. . . optimised these parameters using CCGA-2
. . . improved the intruder detection rate
We plan to. . .
. . . use CACOC in other different scenarios
. . . implement inter-swarm collaborations (different vehicles)
. . . improve this P-P approach using a competitive algorithm
13/13

32. CONCLUSION AND FUTURE WORK
We have. . .
. . . added new features to CACOC (parameters)
. . . optimised these parameters using CCGA-2
. . . improved the intruder detection rate
We plan to. . .
. . . use CACOC in other different scenarios
. . . implement inter-swarm collaborations (different vehicles)
. . . improve this P-P approach using a competitive algorithm
13/13

33. CONCLUSION AND FUTURE WORK
We have. . .
. . . added new features to CACOC (parameters)
. . . optimised these parameters using CCGA-2
. . . improved the intruder detection rate
We plan to. . .
. . . use CACOC in other different scenarios
. . . implement inter-swarm collaborations (different vehicles)
. . . improve this P-P approach using a competitive algorithm
13/13

34. CONCLUSION AND FUTURE WORK
We have. . .
. . . added new features to CACOC (parameters)
. . . optimised these parameters using CCGA-2
. . . improved the intruder detection rate
We plan to. . .
. . . use CACOC in other different scenarios
. . . implement inter-swarm collaborations (different vehicles)
. . . improve this P-P approach using a competitive algorithm
13/13

35. CONCLUSION AND FUTURE WORK
We have. . .
. . . added new features to CACOC (parameters)
. . . optimised these parameters using CCGA-2
. . . improved the intruder detection rate
We plan to. . .
. . . use CACOC in other different scenarios
. . . implement inter-swarm collaborations (different vehicles)
. . . improve this P-P approach using a competitive algorithm
13/13

36. CONCLUSION AND FUTURE WORK
We have. . .
. . . added new features to CACOC (parameters)
. . . optimised these parameters using CCGA-2
. . . improved the intruder detection rate
We plan to. . .
. . . use CACOC in other different scenarios
. . . implement inter-swarm collaborations (different vehicles)
. . . improve this P-P approach using a competitive algorithm
13/13

37. CONCLUSION AND FUTURE WORK
We have. . .
. . . added new features to CACOC (parameters)
. . . optimised these parameters using CCGA-2
. . . improved the intruder detection rate
We plan to. . .
. . . use CACOC in other different scenarios
. . . implement inter-swarm collaborations (different vehicles)
. . . improve this P-P approach using a competitive algorithm
13/13

38. QUESTIONS?
https://hunted.gforge.uni.lu/
https://pcog.uni.lu/
https://wwwen.uni.lu/snt/
https://wwwen.uni.lu/
Optimizing the Performance of an Unpredictable UAV Swarm for Intruder Detection
Daniel H. Stolfi1, Matthias R. Brust1, Grégoire Danoy1,2, Pascal Bouvry1,2
1 SnT - Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Luxembourg
2 FSTM/DCS, University of Luxembourg, Luxembourg
This work relates to Department of Navy award N62909-18-1-2176 issued by the Office of Naval Research. The United States Government has a royalty-free license throughout the
world in all copyrightable material contained herein. This work is partially funded by the joint research programme UL/SnT-ILNAS on Digital Trust for Smart-ICT. The experiments
presented in this paper were carried out using the HPC facilities of the University of Luxembourg – see https://hpc.uni.lu.