Zachary Wilson Computer Science Department University of Nebraska, Omaha Advisor: Dr. Raj Dasgupta

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

 Coverage has to be done efficiently Reducing time, energy (battery) consumed Reducing the amount of repeated coverage of the same region by multiple robots

 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

 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 I should also know what regions other robots have covered till now, so that I can avoid and not re- cover those regions

 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 have to tell other robots what regions I have covered till now so that they don’t re- cover those I should also know what regions other robots have covered till now, so that I can avoid and not re- cover those

 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 have to tell other robots what regions I have covered till now so that they don’t re- cover those I should also know what regions other robots have covered till now, so that I can avoid and not re- cover those Very high communication overhead

 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 have to tell other robots what regions I have covered till now so that they don’t re- cover those I should also know what regions other robots have covered till now, so that I can avoid and not re- cover those More energy (battery), more calculations, more time

 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 This is the region I have just covered

 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 This is the region I have just covered We need to combine these maps...without increasing the number of data points (vertices) used to store the combined map

 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 This is the region I have just covered We need to combine these maps...without increasing the number of data points (vertices) used to store the combined map Otherwise,the maps would keep becoming larger and larger as we cover more regions needing more comms...more battery power and time

 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-  algorithm

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

 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 min. life)  We used 4 simulated test environments:  No obstacles  10% obstacles  25% obstacles  Corridor with rooms.

Snapshots of coverage achieved with 2, 3 or 4 robots 20 X 20 meter 2 arena 2 hours of real time Amount of (instances of) communication between robots in different scenarios

 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%).

 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)

 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