Online Tree-based Planning for Active Spacecraft Fault Estimation and Collision Avoidance

James Ragan¹, Benjamin Riviere, Fred Y. Hadaegh, and Soon-Jo Chung²

The failurePy codebase is provided as part of our Science Robotics research article "Online tree-based planning for active spacecraft fault estimation and collision avoidance". It contains code for simulating spacecraft models subject various faults and algorithms for diagnosing them. Our safe Fault Estimation via Active Sensing Tree search (s-FEAST) algorithm provides a method to actively estimate these faults, even when subject to safety constraints that will be violated shortly.

Dependencies

failurePy's depends on numpy, matplotlib, pyyaml, tqdm, cvxpy and opencv-python. It also depends on Google's JAX project, which can be installed here https://github.com/google/jax#installation (the CPU version is sufficient). Linux and WSL2 in Windows are tested and supported. MacOS is untested, but should work.

Examples

To run an example of our binary or general fault models, run python pipeline.py ./config/binaryFaultModelExample.yaml or python pipeline.py ./config/generalFaultModelExample.yaml in the failurePy directory. These examples demonstrate how our tree search algorithm works, and the configuration file provides an example for how to adapt failurePy to run other simulations.

Data

Output data is stored in the failurePy/SavedData/ folder by default. Data used to generate the paper figures can be found this Dryad depository https://doi.org/10.5061/dryad.xgxd254r1. All data is stored in the form of:

experimentName/solver/nSimulationsPerTree/trialNumber/trialData.dict
experimentName/solver/nSimulationsPerTree/trialNumber/trialData.txt
experimentName/solver/nSimulationsPerTree/averageData.dict
experimentName/config.yaml
experimentName/render.pdf
experimentName/version.txt

Where there can be multiple solvers per experimentName, multiple nSimulationsPerTrees per solver, and multiple trialNumbers per nSimulationsPerTree. The data in trialData.dict and trialData.txt are the same, but are machine vs. human readable. Each .dict file is a python dictionary with the following keys:

field	description
'physicalStateList'	state of the system at each time step
'failureStateList'	constant failure state at each time step
'beliefList'	tuple of weights on each fault and an 3D array representing each conditional filter on these faults
'rewards'	rewards at each time step
'actionList'	action taken to arrive at each time step
'possibleFailures'	the faults considered in this experiment, known to the estimator and solver
'success'	Whether the algorithm converged to the correct fault (and stayed safe)
'steps'	number of time steps taken. Relevant if the simulation can end early
'wallClockTime'	average time to select each action
'treeList'	if saved, the search tree for each time step. Not present in runs with more than one trial per solver and nSimulationsPerTree

The data in averageData.dict is also a python dictionary and represents the average over all trials for the following parameters:

field	description
'avgRewards'	array of the average rewards at each time step
'cumulativeAvgRewards'	array of the average cumulative rewards at each time step
'avgSuccessRate'	average success rate
'avgWallClockTime'	average time in all trials to select each action
'avgSteps'	average steps taken
'varRewards'	array of the variance in the rewards at each time step
'cumulativeVarRewards'	array of the variance in the cumulative rewards at each time step
'varWallClockTime'	the variance in the average time taken to select each action

The config.yaml file provides the parameters used to run this experiment. Combined with version.txt, which logs when the experiment was run, the version of failurePy used, and a hash of the estimator functions used, this allows for repeatability. Finally, the render.pdf file renders the first trial run in each experiment for visualization. Other visualizations can be made as follows:

The code used to create Fig.5 and Fig. S3, is provided in failurePy/visualization/figure5andFigureS3.ipynb. Data is stored in SavedData/figure5 and SavedData/figureS3 in the Dryad depository.

The overlays shown in Figs. 1, S4, S5, and S6 are created by using failurePy/visualization/renderSavedRun.py to render the experimental data, which is also uploaded to the data repository. The same module can be used to re-create Fig. 6 from the raw simulation data provided. Data is stored in SavedData/figure1 in the Dryad depository, the other figures use the same data.

Figs. S1 and S2 can be reproduced from the provided data using failurePy/visualization/figuresS1andS2.ipynb. Data is stored in SavedData/figure1, SavedData/figureS1, SavedData/figureS2 in the Dryad depository.

The visualization directory contains other utilities for visualizing the data that were not used in our paper.

Videos

We have made an overview video that can be seen here:

Citation

The data and code here are for personal and educational use only and provided without warranty; written permission from the authors is required for further use. Please cite our work as follows:

@article{ doi:10.1126/scirobotics.adn4722, author = {James Ragan and Benjamin Riviere and Fred Y. Hadaegh and Soon-Jo Chung }, title = {Online tree-based planning for active spacecraft fault estimation and collision avoidance}, journal = {Science Robotics}, volume = {9}, number = {93}, pages = {eadn4722}, year = {2024}, doi = {10.1126/scirobotics.adn4722}, URL = {https://www.science.org/doi/abs/10.1126/scirobotics.adn4722}, eprint = {https://www.science.org/doi/pdf/10.1126/scirobotics.adn4722}}

Footnotes

1. code base maintainer - jragan@caltech.edu ↩

2. corresponding author - sjchung@caltech.edu ↩