Wavelet-Based Distributed Coverage for Heterogeneous Agents

We develop a coverage approach for heterogeneous agents that leverages the different sensing and motion capabilities of a team. Coverage performance is measured using ergodicity, which when optimized balances exploitation versus exploration, where areas of interest are indicated with an information metric. Prior work uses spectral decomposition of a spatial map of information to guide a set of heterogeneous agents, each with different sensor and motion models, to optimize coverage. This work leverages wavelet transforms to decompose the information map rather than the Fourier transform typically applied to ergodic search and demonstrates the importance of selecting a suitable wavelet family to use, based on the information map being explored. Further a sequence of wavelets is used for decomposition to overcome dependency on selecting one suitable wavelet family. Our experimental results show that using wavelet families well-suited to the specific information map for information map decomposition leads to, on average, 43% improvement over a baseline method in terms of a standard coverage metric (ergodicity), while using a wellsequenced set of wavelets for decomposition leads to a 65% improvement in coverage performance across multiple types of information maps.

Authors

• Ananya Rao
• Howie Choset
• David Wettergreen

Keywords

• Wavelet transforms
• Sequential analysis
• Fourier transforms
• Wavelet domain
• Systematics
• Wavelet analysis
• Path planning
• Sensors
• Resource management
• Standards
• Heterogeneous Agents
• Fourier Transform
• Wavelet Transform
• Baseline Methods
• Motion Model
• Coverage Performance
• Types Of Agents
• Gaussian Mixture Model
• Dirac Delta
• Fourier Series
• Classification Maps
• Square Integrable
• Coefficient Of Group
• Continuous Wavelet Transform
• Kin Selection
• Wavelet Decomposition
• Dimensional Distribution
• Search Region
• Decomposition Level
• Approximation Coefficients
• Cuprite
• Overall Improvement In Performance
• Search Problem
• Use Of Decomposition
• Road Network
• Smooth Data
• Distribution Of Agents
• Hierarchical Decomposition