Dynamic Routing and Robotic Coordination for Oceanographic Adaptive Sampling

The collaborative NSF grant IIS-1035917 "Dynamic Routing and Robotic Coordination for Oceanographic Adaptive Sampling" aims to design innovative routing, planning and coordination strategies for robot networks, and their application to oceanography. The approach is organized in three synergistic thrusts: (1) the ap- plication of queueing theory and combinatorial techniques to networked robots performing sequential tasks, (2) the design of novel distributed optimization and coordination schemes relying only on asynchronous and asymmetric communication, (3) the design of practical routing and coordination algorithms for the USC Net- worked Aquatic Platforms. In collaboration with oceanographers and marine biologists, the project aims to design motion, communication and interaction protocols that maximize the amount of scientific information collected by the platforms. This proposal addresses multi-dimensional problems of relevance in Engineering and Computer Science by unifying fundamental concepts from multiple cyberphysical domains (robotics, autonomy, combinatorics, and network science).

Recent progress will be described in this poster on the following topics:

1. Cooperative patrolling strategies

   F. Pasqualetti, J. W. Durham, and F. Bullo, "Cooperative Patrolling via Weighted Tours: Performance Analysis and Distributed Algorithms," IEEE Transaction on Robotics, 2012, (to appear)

2. Route planning through stochastic neighborhoods
   P. Kamousi and S. Suri, "Euclidean Traveling Salesman Tours through Stochastic Neighborhoods," Sym-

   posium on Discrete Algorithms (SODA 2013), (submitted)

3. Minimum-risk path planning with ocean current predictions via switching MDP planner

   G. Hollinger, A. M. Pereira, V. Ortenzi, and G. S. Sukhatme, "Towards improved prediction of ocean processes using statistical machine learning," Robotics: Science and Systems, Workshop on Robotics for Environmental Monitoring, Sydney, Australia, July 2012.

Award ID: 1035917