Multi-Agent Coverage Path Planning - Wildlife Monitoring and Beyond

Explore multi-agent coverage path planning for wildlife monitoring and beyond in this research seminar from Stanford University. Delve into the challenges of autonomous aerial surveys, focusing on applications like monitoring Adelie penguin colonies in Antarctica. Learn about novel algorithms using satisfiability modulo theory (SMT) to solve complex path planning problems with multiple agents and real-world constraints. Discover how these techniques can be applied to wildlife conservation, field operations, and other disciplines requiring efficient area coverage. Gain insights into the practical implementation of autonomous survey planning, including considerations for take-off and landing locations, battery life, and legal restrictions. Follow the journey from theoretical development to field testing in harsh Antarctic conditions, and explore future directions for optimizing survey fleets, adapting to changing environments, and expanding applications beyond wildlife monitoring.

Introduction
Why Wildlife Monitoring
Conducting an Effective Penguin Census
Antarctica
Penguins
Field camp
Sea ice
Cape Crosser
Path Planning Problem
Methods
SMT
Coverage constraint improvements
Rectangular suctioning
Cyclical pads
Linking subgraphs
Path graph
Battery life
Graph partitioning
Selecting nodes
Legal restrictions
Results
Project Status
Field Tests
Penguin Tracking
Field Testing
Water Loss
Emergency Landings
Drones
Next Steps
Optimize for Specific Size Fleet
Small Drones
Loop Closure
Survey Time
Path Planning for Other Platforms
Path Planning for Changing Environments
New Applications
Other Applications
Future Field Trips
Can you do it from here
Batteryfree options
Deploying sensors
Losing a drone