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Marine Environmental Monitoring Augustine Ekweariri University of - - PowerPoint PPT Presentation

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MIN Faculty Department of Informatics Application of Swarm Robotics Systems to Marine Environmental Monitoring Augustine Ekweariri University of Hamburg Faculty of Mathematics, Informatics and Natural Sciences Department of Informatics

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Application of Swarm Robotics Systems to Marine Environmental Monitoring

University of Hamburg Faculty of Mathematics, Informatics and Natural Sciences Department of Informatics Technical Aspects of Multimodal Systems

14.01.2019

Augustine Ekweariri

MIN Faculty Department of Informatics

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Outline

  • Motivation
  • Introduction & Fundamentals
  • Methodology
  • Experimental setup
  • Results
  • Discussion
  • Conclusion

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Motivation

“We know more about the moon surface than the earth’s ocean

  • Most talks about climate change is about land
  • What about pollution, overfishing, ocean acidification, etc?

[1]

Fig 1: The melting Arctic ice cap - [2]

Fig 2: ~ 90% of seabirds have eaten plastics in their lives – [3]

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Motivation Cont.

  • Vital in application areas such as
  • Search and rescue
  • Surveillance
  • Clean up
  • Few marine vehicles

Fig 3: The Ocean Cleanup organization has a plan to start cleaning it up since march 2018 - [4] Augustine Ekweariri: Application of Swarm Robotics Systems to Marine Environmental Monitoring 4

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What is a swarm?

  • Group of agents that;
  • ….are not centrally controlled
  • ….agents are relatively inefficient
  • ….have local sensing
  • Not all groups are swarm

Fig 4: Basics in evolution of collective behavior in swarm robots [6] Augustine Ekweariri: Application of Swarm Robotics Systems to Marine Environmental Monitoring 5

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Properties of a Swarm

  • Robustness
  • Ability to cope with faults of others
  • Versatility
  • Ability to operate in a variety of different environment or assume different

task

  • Scalability
  • Ability to maintain the group behavior regardless of the swarm size
  • Emergent behavior through local interaction

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Aquatic Swarm Robotic System

  • Advantageous in;
  • Environmental monitoring
  • Marine life localization
  • Sea boarder patrolling
  • Why?
  • Distributed sensing
  • High spatial resolution
  • Difficult to carry out with a single or few boats.

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Application of Swarm Robotics Systems to Marine Environmental Monitoring – D. Miguel et al

Fig 5: Eight samples of the out of 10 robots [1]

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Prototype

Fig 6: Prototype of the robot – [6]

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Synthesizing the controllers

  • Much of a Challenge

Why?

  • The parameters for local interaction are hard to hardcode.

Methods

  • Neural Networks
  • Reinforcement learning
  • Evolutionary computation

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Evolutionary Synthesis of Controller

  • Evolutionary Robotics
  • Studies the application of evolutionary computing to synthesis of robot

controllers

  • ER is a preferred alternative to manual programming
  • Given a specific task ER algorithm evaluates & optimizes controllers
  • Thereby facilitating the emergence of self organizing behavior

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Behaviors

  • The following behaviors should emerge;
  • Homing
  • Navigate to a waypoint without collision
  • Clustering
  • Robots must find each other and form a group
  • Dispersion
  • Robots must get as far away from one another as possible & remain in communication range
  • Monitoring
  • Robots must cover a predefined area

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Methodology

Simulation

  • Conducted offline
  • JBotEvolver
  • Parameters

= measurement from real robots + noise

  • Robot controlled by ANN
  • Input = sensory data
  • Output is speed + heading pos covert to propellers
  • Configuration of ANN is optimized by NEAT

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In a nutshell

Fig 7: Summary of the process – [6]

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Environmental Monitoring

  • Define a geo-fence
  • Robots start from base station
  • Complete task and return
  • Area divided into grid cells 100x100m
  • Area must be visited by at least one robot

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Experiments - Area of coverage

  • Square: A square area with 2.5 km × 2.5 km
  • Rectangle: A rectangular area with 4.2 km × 1.5 km
  • L-Shape: A square area with 2.9 km × 2.9 km with a cutout of 1.45 km

× 1.45 km

  • Areas divided into 100 X 100 grid
  • grid must be visited at least one robot

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Experiments - Area of coverage cont.

Proportion of the area covered over time, averaged over the three different areas, and ten simulation samples for each area. (Simulation) [1]

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Fig 8: Coverage area and heatmap – [1]

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Experiments - Temperature monitoring

Coverage area Heat map

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Fig 9: Temperature monitoring and heatmap – [1]

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Experiments - Temperature monitoring cont.

Coverage area Heat map

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Fig 10: Temperature monitoring and heatmap – [1]

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Experiments – Robustness to fault

  • Tested by injecting faults to robots
  • Each simulation step, probability of robot failing
  • Probability to recover from fault

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Experiments – Robustness to fault cont.

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Coverage of the area for a mission time of 240 minutes with temporary faults (simulation) [1] Fig 11: Robustness to fault – [1]

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Results

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0151834

  • Homing

Tested on four waypoints Waypoints = 40m apart Time: 4 mins each

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Results cont.

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0151834

  • Dispersion

8 robots placed in a cluster They need to disperse 20m apart Time: 90 secs each

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Results cont.

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0151834

  • Clustering

Robots placed in an area of 100x100 40ms apart from each other Time: 180 secs

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Discussion

  • Properties of Swarm evident
  • Robustness: Observed during monitoring tasks
  • Flexibility: Observed during different coordination tasks
  • Scalability: Robots were removed
  • Swarm behavior emerged during each task
  • Swarm robotics for submarine mission under research.

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Conclusion

  • Properties of Swarm robotics demonstrated
  • Operating in real environment
  • Result in simulation similar to real robots
  • Verified key properties of swarm robotics

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Thanks for your attention

Questions

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References

  • 1. Duarte, M., Gomes, J., Costa, V., Rodrigues, T., Silva, F., Lobo, V., … Christensen, A. L. (2016). Application of

swarm robotics systems to marine environmental monitoring. OCEANS 2016 - Shanghai, 1–8. https://doi.org/10.1109/OCEANSAP.2016.7485429

  • 2. http://qeprize.org/createthefuture/bps-new-robot-fleet-monitoring-underwater-world/
  • 3. https://psmag.com/environment/climate-change-affects-the-71-percent-of-the-world-people-dont-live-on-

too-64659

  • 4. http://goodnature.nathab.com/video-oceans-and-plastics-pollution/
  • 5. https://alamedapointenviro.com/2018/03/25/ocean-cleanup-project-to-launch-from-alameda-point/
  • 6. Duarte, M., Costa, V., Gomes, J., Rodrigues, T., Silva, F., Oliveira, S. M., & Christensen, A. L. (2016). Evolution
  • f collective behaviors for a real swarm of aquatic surface robots. PLoS ONE, 11(3), 1–25.

https://doi.org/10.1371/journal.pone.0151834

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