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Machine Learning Based Path Planning for Improved Rover Navigation

Abcouwer, Neil and Daftry, Shreyansh and del Sesto, Tyler and Toupet, Olivier and Ono, Masahiro and Venkatraman, Siddarth and Lanka, Ravi and Song, Jialin and Yue, Yisong (2021) Machine Learning Based Path Planning for Improved Rover Navigation. In: 2021 IEEE Aerospace Conference. IEEE , Piscataway, NJ, pp. 1-9. ISBN 978-1-7281-7436-5.

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Enhanced AutoNav (ENav), the baseline surface navigation software for NASA's Perseverance rover, sorts a list of candidate paths for the rover to traverse, then uses the Approximate Clearance Evaluation (ACE) algorithm to evaluate whether the most highly ranked paths are safe. ACE is crucial for maintaining the safety of the rover, but is computationally expensive. If the most promising candidates in the list of paths are all found to be infeasible, ENav must continue to search the list and run time-consuming ACE evaluations until a feasible path is found. In this paper, we present two heuristics that, given a terrain heightmap around the rover, produce cost estimates that more effectively rank the candidate paths before ACE evaluation. The first heuristic uses Sobel operators and convolution to incorporate the cost of traversing high-gradient terrain. The second heuristic uses a machine learning (ML) model to predict areas that will be deemed untraversable by ACE. We used physics simulations to collect training data for the ML model and to run Monte Carlo trials to quantify navigation performance across a variety of terrains with various slopes and rock distributions. Compared to ENav's baseline performance, integrating the heuristics can lead to a significant reduction in ACE evaluations and average computation time per planning cycle, increase path efficiency, and maintain or improve the rate of successful traverses. This strategy of targeting specific bottlenecks with ML while maintaining the original ACE safety checks provides an example of how ML can be infused into planetary science missions and other safety-critical software.

Item Type:Book Section
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Yue, Yisong0000-0001-9127-1989
Additional Information:© 2021 IEEE. The research described in this paper was performed at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004). The authors would like to thank the JPL Research and Technology Development (R&TD) program for supporting this research.
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JPL Research and Technology Development FundUNSPECIFIED
Record Number:CaltechAUTHORS:20210119-161632609
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Official Citation:N. Abcouwer et al., "Machine Learning Based Path Planning for Improved Rover Navigation," 2021 IEEE Aerospace Conference (50100), 2021, pp. 1-9, doi: 10.1109/AERO50100.2021.9438337
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:107566
Deposited By: George Porter
Deposited On:20 Jan 2021 15:47
Last Modified:14 Sep 2021 23:11

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