Machine Learning Based Path Planning for Improved Rover Navigation
Neil Abcouwer,Shreyansh Daftry,Tyler del Sesto,Olivier Toupet,Masahiro Ono,Siddarth Venkatraman,Ravi Lanka,Jialin Song,Yisong Yue +8 more
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TLDR
In this paper, the authors present two heuristics that, given a terrain heightmap around the rover, produce cost estimates that more effectively rank the candidate paths before ACE evaluation.Abstract:
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.read more
Citations
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Journal ArticleDOI
MLNav: Learning to Safely Navigate on Martian Terrains
Shreyansh Daftry,Neil Abcouwer,Tyler del Sesto,Siddarth Venkatraman,Jialin Song,Lucas Igel,Amos Byon,Ugo Rosolia,Yisong Yue,Masahiro Ono +9 more
TL;DR: Compared to the baseline ENav path planner on board the Perserverance rover, MLNav can provide a significant improvement in multiple key metrics, such as a 10x reduction in collision checks when navigating real Martian terrains, despite being trained with synthetic terrains.
Journal ArticleDOI
The Neural Process Family: Survey, Applications and Perspectives
TL;DR: A comprehensive survey of NPF models is needed to organize and relate their motivation, methodology, and experiments and shed light on their potential to bring several recent advances in other deep learning domains under one umbrella.
Posted Content
Machine Learning for Mars Exploration
TL;DR: The use of machine learning techniques for Mars exploration has been discussed in this paper, where the authors summarize the general features and phenomena of Mars to provide a general overview of the planet, elaborate upon uncertainties of Mars that would be beneficial to explore and understand, summarize every current or previous usage of ML techniques in the exploration of Mars, explore implementations of ML that will be utilized in future Mars exploration missions, and explore machine learning methods used in Earthly domains to provide solutions to the previously described uncertainties.
Proceedings ArticleDOI
Testing Mars 2020 Flight Software and Hardware in the Surface System Development Environment
TL;DR: The Surface System Development Environment (SSDEV) as mentioned in this paper was developed for the Mars 2020 Perseverance Rover to support extensive flight software and flight hardware testing, and used it for a wide variety of testing.
Journal ArticleDOI
Semi-supervised Learning for Mars Imagery Classification and Segmentation
TL;DR: A semi-supervised framework for machine vision on Mars is introduced and the learning strategies can improve the classification and segmentation models by a large margin and outperform state-of-the-art approaches.
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