multi-robot coverage with dynamic coverage information compression
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Zachary Wilson Computer Science Department University of Nebraska, Omaha Advisor: Dr. Raj Dasgupta. Multi-robot Coverage with Dynamic Coverage Information Compression. - PowerPoint PPT PresentationTRANSCRIPT
MULTI-ROBOT COVERAGE WITH DYNAMIC COVERAGE INFORMATION COMPRESSION
Zachary Wilson
Computer Science DepartmentUniversity of Nebraska, Omaha
Advisor: Dr. Raj Dasgupta
MULTI-ROBOT TERRAIN COVERAGE PROBLEM
• 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
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
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
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
I should also know what regions other robots have covered till now, so that I can avoid and not re-cover those regions
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 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
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 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
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 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
MULTI-ROBOT COVERAGE INFORMATION SHARING Every robot covers a certain region on its own
(autonomously)
MULTI-ROBOT COVERAGE INFORMATION SHARING 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
This is the region I have just covered
MULTI-ROBOT COVERAGE INFORMATION SHARING 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
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
MULTI-ROBOT COVERAGE INFORMATION SHARING 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
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
mapOtherwise,the maps would keep becoming larger and larger as we
cover more regions needing more comms...more battery power and time
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
COVERAGE COMPRESSION: OVERLAPPING REGIONS Fitness function used to
accept or discard fitted polygon
Adjusting weights gives different amount of repeated coverage based on application domainLandmine
detection: Repeated coverage is not fatal, could improve detection accuracy
Pesticide application: Repeated coverage can kill crops
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.
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
SIMULATION RESULTS: HOW WELL DOES THE COVERAGE PERFORM
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 INFORMATION COMPRESSION WORK 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 AND FUTURE WORK 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
