UNDERWATER DRONES CONTROL TOWER UNDERWATER DRONES CONTROL TOWER

Transcription

1 UNDERWATER DRONES CONTROL TOWER UNDERWATER DRONES CONTROL TOWER Adnan Tahirovic - Kemal Delic ARCHITECTURE, DESIGN, ENGINEERING

2 Talk outline - why and how uwr is important? Underwater World Explained Technology Challenges and Future Developments Architecture, Design, Engineering Introduced Control Tower Architecture : Conceptual View Cloud Based Analytics : Design View Mapping the Sea Bottom - Navigation Intelligent Underwater Drone Design : Swarming Live Demo : Navigation Algorithm Explained Future Explored

3 Underwater World Explained Hidden Face of Oceans

4 Oceans in numbers Depth of Ignorance - Level of Dependency LIVING SPECIES 91% still unknown, 13% only catalogued RESOURCES 90% of transportation, 20% of animal proteins PHYSICAL WORLD 71% Earth surface water, 93% of heat stored 80% of volcanic activities under water

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6 Eight challenges - unknowns Thermal - temperature anomalies, impacting life on earth Geological - cobalt, platinum etc found in proximity of volcanoes Genetic - new molecules for drugs learned from deep sea creatures Ecological - destruction of habitats Climate - likely impact of human activities Hydrodynamic - Golf Stream 15% lower circulation Chemical - seas and oceans may become toxic or sterile Physical - rise of the sea level, 60% megapolis on the coast

7 Map of unknown worlds Nobody knows for sure what might be below seabed where, how much it is worth Strategic future exploration Always preceded by map creation

8 New molecules Discoveries of new drugs and materials Species living in extreme conditions, no sunlight, huge pressure and cold

9 Gulf stream change Climate Change Might be caused by Changes in ocean Streams? Last 3 centuries

10 Underwater World stratified

11 Into the abyss 0m 4000 m 8145 m deepest fish (snailfish) found ALVIN (USA) 100 m 8400 m Puerto Rico trench Depths for divers 500 m 1000 m Depths for submarines MIR (Russia) Nautile (France) Sentry HROV (USA) (deepest point in the Atlantic Ocean) 6000 m 97% of ocean depths are less than 6000m ABISMO ROV (Japan) 2000 m Shinkai 6500 (Japan) 6500 m m Jason ROV (USA) m Mariana Trench 3000 m Maximal dive for whales (Curver beaked whale) 7000 m Jiaolong (China) Area: 361,000,000 km2 (71% of Earth surface) (Deepest point in the Pacific Ocean) Mariana Trench reached by: Bathyscaph Trieste on Kaiko ROV on lost in 2003 Nereus HROV on lost in 2012 Deepsea challenger on

12 Technology Challenges and Future Developments Last week in Croatia.. Breaking The Surface Years anniversary workshop

13 Game changing technologies that have the potential to significantly enhance capabilities of systems and transform how we will use future systems Quantum computing, neuromorphic (brain inspired computing), Microelectronics, (components built of molecules), Robotics, Soft reconfigurable robotics,. Nanomaterials, advanced materials,. Genetics,. Big data, Alternative energy sources, Artificial intelligence, machine learning,. Modeling and simulation,...

14 Ec subucultron project Venice - Laguna Health Monitoring

15 Architecture, Design, Engineering Introduced

16 Control Tower Architecture : Conceptual View

17 Control Tower Architecture : Conceptual View

18 CT: Cloud Based Analytics : Design View

19 Intelligent Underwater Drone Design : concept

20 Vision dl explained Picture Recognition

21 Olfaction with DL NN Odor Recognition

22 Intelligent Underwater Drone Design

23 Deep dive follows..

24 A possible mission: Coverage path planning (CPp) Monitoring Surveillance Hazard detection Planetary exploration Rescue Cleaning De-mining Fire extinguishing Agricultural spraying Tahirovic Adnan and Alessandro Astolfi. "A convergent solution to the multi-vehicle coverage problem." American Control Conference (ACC), IEEE, 2013.

25 CPp Algorithm Explained Unconstrained environment Fully-connected swarm

26 CPp Algorithm Explained

27 CPp Algorithm Explained Partially-connected swarm

28 CPp Algorithm Explained Tahirovic Adnan, et al. "A receding horizon scheme for constrained multi-vehicle coverage problems." Systems, Man, and Cybernetics (SMC), 2016 IEEE International Conference on. IEEE, 2016.

29 CPp Algorithm Simulation

30 CPp Algorithm Features Simple!!! Cooperative!!! Scalable!!! Robust!!! Adaptive??? All necessary features of swarm intelligence obtained via simple agent s rules.

31 Rapidly Exploring Random Trees

32 Rapidly exploring random vines Tahirovic Adnan, and Mina Ferizbegovic. "Rapidly-Exploring Random Vines (RRV) for Motion Planning in Configuration Spaces with Narrow Passages." 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018.

33 Rapidly exploring random vines

34 Rapidly exploring random vines

35 Rapidly exploring random vines

36 Rapidly exploring random vines

37 Key takeaways Shift from ROV/AUV devices to the entire ecosystem The rise of multi-modal systems : flying, sailing, diving Big Data collections waiting for better analytics Security nearly non-existent Biology inspired sensory and communication systems Ocean explorations will be even more important in the future and AI approaches and ML methods will play crucial role - from intelligent swarm edge devices to elaborate analytics in the cloud

38 Back up slides

39 Future explored - what s next? Monitoring of Bosnian lakes Health monitoring of Venetian Lagoon Monitoring of fishponds in Norway Monitoring underwater cables