1 / 20

Multi-robot Coverage with Dynamic Coverage Information Compression

Zachary Wilson Computer Science Department University of Nebraska, Omaha Advisor: Dr. Raj Dasgupta. Multi-robot Coverage with Dynamic Coverage Information Compression.

agalia
Download Presentation

Multi-robot Coverage with Dynamic Coverage Information Compression

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Zachary Wilson Computer Science Department University of Nebraska, Omaha Advisor: Dr. Raj Dasgupta Multi-robot Coverage with Dynamic Coverage Information Compression

  2. Problem statement: How to coordinate a set of robots so that they can completely cover an initially unknown region within which they are deployed • Encountered in many applications of robotic systems • Detecting landmines for humanitarian demining • Unmanned search and rescue following disasters • Extra-terrestrial exploration • Domestic applications: automated lawn mowing, vacuum cleaning, etc Multi-robot Terrain Coverage Problem

  3. Communication Overhead in Multi-robot Terrain Coverage • Coverage has to be done efficiently • Reducing time, energy (battery) consumed • Reducing the amount of repeated coverage of the same region by multiple robots

  4. Communication Overhead in Multi-robot Terrain Coverage • Coverage has to be done efficiently • Reducing time, energy (battery) consumed • Reducing the amount of repeated coverage of the same region by multiple robots I have to tell other robots what regions I have covered till now so that they don’t re-cover those

  5. Communication Overhead in Multi-robot Terrain Coverage • Coverage has to be done efficiently • Reducing time, energy (battery) consumed • Reducing the amount of repeated coverage of the same region by multiple robots I should also know what regions other robots have covered till now, so that I can avoid and not re-cover those regions I have to tell other robots what regions I have covered till now so that they don’t re-cover those

  6. Communication Overhead in Multi-robot Terrain Coverage • Coverage has to be done efficiently • Reducing time, energy (battery) consumed • Reducing the amount of repeated coverage of the same region by multiple robots • How much info do robots communicate? • Maps exchanged between every pair of robots • Repeated at certain intervals • Map of covered region for each robot keeps growing with time I should also know what regions other robots have covered till now, so that I can avoid and not re-cover those I have to tell other robots what regions I have covered till now so that they don’t re-cover those

  7. Communication Overhead in Multi-robot Terrain Coverage • Coverage has to be done efficiently • Reducing time, energy (battery) consumed • Reducing the amount of repeated coverage of the same region by multiple robots • How much info do robots communicate? • Maps exchanged between every pair of robots • Repeated at certain intervals • Map of covered region for each robot keeps growing with time I should also know what regions other robots have covered till now, so that I can avoid and not re-cover those I have to tell other robots what regions I have covered till now so that they don’t re-cover those Very high communication overhead

  8. Communication Overhead in Multi-robot Terrain Coverage • Coverage has to be done efficiently • Reducing time, energy (battery) consumed • Reducing the amount of repeated coverage of the same region by multiple robots • How much info do robots communicate? • Maps exchanged between every pair of robots • Repeated at certain intervals • Map of covered region for each robot keeps growing with time I should also know what regions other robots have covered till now, so that I can avoid and not re-cover those I have to tell other robots what regions I have covered till now so that they don’t re-cover those More energy (battery), more calculations, more time

  9. Every robot covers a certain region on its own (autonomously) Multi-robot Coverage Information Sharing

  10. Every robot covers a certain region on its own (autonomously) Communicates this coverage map to other robots within communication range Receives other robots’ coverage maps Multi-robot Coverage Information Sharing This is the region I have just covered This is the region I have just covered

  11. Every robot covers a certain region on its own (autonomously) Communicates this coverage map to other robots in communication range Receives other robots’ coverage maps Multi-robot Coverage Information Sharing We need to combine these maps This is the region I have just covered ...without increasing the number of data points (vertices) used to store the combined map This is the region I have just covered

  12. Every robot covers a certain region on its own (autonomously) Communicates this coverage map to other robots in communication range Receives other robots’ coverage maps Multi-robot Coverage Information Sharing We need to combine these maps This is the region I have just covered ...without increasing the number of data points (vertices) used to store the combined map This is the region I have just covered Otherwise,the maps would keep becoming larger and larger as we cover more regions needing more comms...more battery power and time

  13. Coverage Information Compression • Take two or more polygons • Calculate their bounding convex polygon – called convex hull • Make an approximation of the convex hull that has a fixed (constant) number of points – using min-e algorithm

  14. Fitness function used to accept or discard fitted polygon Adjusting weights gives different amount of repeated coverage based on application domain Coverage Compression: Overlapping Regions Landmine detection: Repeated coverage is not fatal, could improve detection accuracy Pesticide application: Repeated coverage can kill crops

  15. Simulation Environment • The Corobot platform: • Stargazer localization module (gives 2-d coordinates) • 5 IR sensors (for avoiding fixed obstacles – walls) • 640x480 camera (used for avoiding moving objects – other robots) • Wi-Fi wireless comms. • 10 AH battery (about 20-30 min. life) • We used 4 simulated test environments: • No obstacles • 10% obstacles • 25% obstacles • Corridor with rooms.

  16. Simulation Results: How Well Does the Coverage Perform • Snapshots of coverage achieved with 2, 3 or 4 robots • 20 X 20 meter2 arena • 2 hours of real time Amount of (instances of) communication between robots in different scenarios

  17. Coverage Efficiency: • The first graph shows the useful distance traveled while doing coverage. • The second graph shows the overhead distance, e.g., moving between regions while not doing coverage. • We see that as the number of obstacles increases, the amount of overhead increases while the amount of coverage decreases. • Peak efficiency is about 2.67 meters of coverage for every meter of overhead (72%). Simulation Results: How Well Does the Coverage Perform

  18. Compression Efficiency: • The first graph shows the compression offered by standard error-free ZIP compression from 4 to 200 data points. • The second graph shows the integrity of data compressed with the min-ε algorithm for different statically-sized approximations. • With a 200 point data-set: • ZIP algorithm: 2% decrease in size, 0% loss • Min-ε algorithm: 98% decrease in size, 10% loss (with a 4 point approximation) Simulation ResultS: How Well Does the INFORMATION COMPRESSION WORK

  19. Conclusions: • Efficient coverage through communication • Efficient communication through compression • Efficient compression through approximation • Hardware implementation also done on Corobot robots • Future work: • More efficient region selection • Neural-network based fitness determination • Comparison with other techniques • Acknowledgements: We are grateful to the U.S. Office of Naval Research for sponsoring this research through the COMRADES project Conclusions and Future Work

More Related