SGP-based Optimization Tools

Software Suite for Sensor Placement (SP) and Informative Path Planning (IPP).

The library includes python code for the following:

• Greedy algorithm-based approaches
• Bayesian optimization-based approaches
• Genetic algorithm-based approaches
• Sparse Gaussian process (SGP)-based approaches

       

Installation

The library is available as a pip package. To install the package, run the following command:

sudo apt-get install libhdf5-dev python3-pip -y
python3 -m pip install sgptools

Installation from source:

sudo apt-get install libhdf5-dev python3-pip -y
python3 -m pip install -r requirements.txt
python3 -m pip install -e .

Quick Start

Please refer to the demos folder for Jupyter notebooks demonstrating all the methods included in the library 😄

Method Summary

Codemap

• demos/: Jupyter notebooks with code to demonstrate each method in the library
  • IPP.ipynb: SGP-based IPP (point, non-point, continuous sensing, distance constrained, and multi-robot)
  • IPPBaselines.ipynb: SGP-based IPP approach and baseline methods
  • non_point_FoV.ipynb: IPP with non-point FoV sensors (drone camera setup)
  • non_stationary_demo.ipynb: SP with non-stationary kernel
  • obstacles.ipynb: SP in an environment with obstacles
• sgptools/: SGP-Tools library
  • kernels/: Kernel functions
    • neural_kernel.py: Neural Non-Stationary Spectral Kernel
  • models/: Sensor placement and IPP methods
    • core/: GP/SGP models used for sensor placement and IPP
      • augmented_gpr.py: GPflow's GP that supports transforms (expansion and aggregation)
      • augmented_sgpr.py: GPflow's SGP that supports transforms (expansion and aggregation)
      • transformations.py: Expansion and aggregation transforms for IPP
    • bo.py: Bayesian optimization-based sensor placement method that maximizes mutual information
    • cma_es.py: Genetic algorithm-based sensor placement method that maximizes mutual information
    • continuous_sgp.py: Continuous SGP-based sensor placement method
    • greedy_mi.py: Greedy sensor placement method that maximizes mutual information
    • greedy_sgp.py: Greedy SGP-based sensor placement method
  • utils/: Tools used for preprocessing the data, training GPs and SGPs, and generating paths
    • data.py: Tools to preprocess datasets
    • gpflow.py: Tools to interface with GPflow
    • metrics.py: Metrics used to quantify the solution quality
    • misc.py: Miscellaneous helper functions
    • tsp.py: TSP solver

Datasets

• The Intel lab temperature dataset can be downloaded from here
• The ROMS ocean salinity dataset can be downloaded from here
• The Precipitation dataset can be downloaded from here
• The US soil moisture dataset can be downloaded from here
• The US elevation dataset can be downloaded from here

About SGP-Tools

Please consider citing the following papers if you use SGP-Tools in your academic work 😄

@misc{JakkalaA23SP,
AUTHOR={Kalvik Jakkala and Srinivas Akella},
TITLE={Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces},
NOTE= {Preprint},
YEAR={2023},
URL={https://itskalvik.github.io/publication/sgp-sp},
}

@inproceedings{JakkalaA24IPP,
AUTHOR={Kalvik Jakkala and Srinivas Akella},
TITLE={Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes},
booktitle={IEEE International Conference on Robotics and Automation, {ICRA}},
YEAR={2024},
PUBLISHER = {{IEEE}},
URL={https://itskalvik.github.io/publication/sgp-ipp}
}

Acknowledgements

This work was funded in part by the UNC Charlotte Office of Research and Economic Development and by NSF under Award Number IIP-1919233.

License

The SGP-Tools software suite is licensed under the terms of the Apache License 2.0. See LICENSE for more information.