Abstract
This chapter presents a strategy to enable a team of mobile robots to adaptively sample and track a dynamic spatiotemporal process.We propose a distributed strategy where robots collect sparse sensor measurements, create a reduced-order model (ROM) of the spatiotemporal process, and use this model to estimate field values for areas without sensor measurements of the dynamic process. The robots then use these estimates of the field, or inferences about the process, to adapt the model and reconfigure their sensing locations. We use this method to obtain an estimate for the underlying flow field and use that to plan optimal energy paths for robots to travel between sensing locations. We show that the errors due to the reduced-order modeling scheme are bounded, and we illustrate the application of the proposed solution in simulation and compare it to centralized and global approaches. We then test our approach with physical marine robots sampling a spatially nonuniform time-varying process in a water tank.
| Original language | English |
|---|---|
| Title of host publication | Autonomous Underwater Vehicles |
| Publisher | Institution of Engineering and Technology |
| Pages | 493-537 |
| Number of pages | 45 |
| ISBN (Electronic) | 9781785617034 |
| DOIs | |
| State | Published - 1 Jan 2020 |
Keywords
- Adaptive control
- Adaptive sampling
- Control system analysis and synthesis methods
- Distributed strategy
- Dynamic spatiotemporal process
- Energy-efficient navigation
- Marine system control
- Marine systems
- Mobile robot team
- Mobile robots
- Mobile robots
- Multi-robot systems
- Optimal energy path planning
- Other topics in statistics
- Path planning
- Physical marine robots
- ROM
- Reduced order systems
- Reduced-order model
- Sampling methods
- Self-adjusting control systems
- Sparse sensor measurements
- Spatial variables control
- Spatially nonuniform time-varying process
- Time-varying control systems
- Time-varying flows
- Time-varying systems
- Water tank
