This work investigates multiagent path planning in strong, dynamic currents using thousands of highly underactuated vehicles. We address the specific task of path planning for a global network of ocean-observing floats. These submersibles are typified by the Argo global network consisting of over 3000 sensor platforms. They can control their buoyancy to float at depth for data collection or rise to the surface for satellite communications. Currently, floats drift at a constant depth regardless of the local currents. However, accurate current forecasts have become available which present the possibility of intentionally controlling floats' motion by dynamically commanding them to linger at different depths. This project explores the use of these current predictions to direct float networks to some desired final formation or position. It presents multiple algorithms for such path optimization and demonstrates their advantage over the standard approach of constant-depth drifting.
['Kristen P. Dahl', 'David R. Thompson', 'David McLaren', 'Yi Chao', 'Steve Chien']
Current-sensitive path planning for an underactuated free-floating ocean Sensorweb