Optimal Use of Machine Learning for Planetary Terrain Navigation
Creators: name: BLACKER, PETER CHARLES
Publisher: name: University of Surrey
Year: 2021

description: "Machine Learning (ML) is spreading into more application areas and facilitating a step change
description: in autonomous perception and comprehension capabilities. Within the space sector it is currently
description: used in the ground segment and its use in the space segment is being actively investigated. ML is
description: especially good at perception tasks that have traditionally been difficult for computers to master,
description: improvements in these perception capabilities have facilitated a wide range of new terrestrial
description: applications such as autonomous vehicles and drones as well as language comprehension and
description: translation. Deep space probes and rovers are reliant upon their on-board autonomy since
description: communication opportunities are sporadic, low bandwidth, and high latency. The levels of
description: autonomy these craft have directly affects their capabilities, enabling them to perform activities
description: without direct commands from ground controllers.
description: ML models have the potential to increase the level of spacecraft autonomy, expanding mission
description: capabilities, science returns, and returns on investment however two formidable barriers to
description: adoption exist. Firstly confidence in ML as a discipline and of the performance of specific
description: models is lower than that usually expected in the aerospace community, careful mission design
description: is required to demonstrate and especially utilise ML on active space missions. Secondly limited
description: processing power of space qualified radiation tolerant processors presents challenges not seen in
description: many terrestrial applications to date. On-board anomaly detection and robotic perception are two
description: applications where the use of ML on-board space vehicles and rovers is currently undergoing
description: active research and development.
description: To the authors knowledge this thesis presents the first investigation into the use of ML for the
description: estimation of terrain navigability on-board planetary rovers. The suitability of both Convo-
description: lutional Neural Network (CNN)s and encoder-decoder models is evaluated in terms of their
description: accuracy and computational performance using two planetary terrain data-sets. Their accuracy
description: was found to match that of existing state of the art navigability estimators, while surpassing
description: their performance. Deployment of ML models onto radiation hardened processors is identified
description: as barrier to adoption, since no software tools existed which targeted these processors.
description: Experimental tools automating the deployment and validation tasks are developed, which are
description: the first of their kind to target radiation hardened processors. Enabling new insights in the study
description: of low level ML implementation on current and next generation radiation hardened processors.
description: Using these tools novel techniques are found that significantly reduce the amount of memory
description: required to perform inference on a wide range of contemporary benchmark models, while using
description: state of the art techniques to optimise execution time. The memory requirement of MobileNet
description: v1 is reduced by 33% while MobileNet v2 is reduced by 53%. The impacts of new techniques
description: are been analytically characterised, and automated tools developed which allow rapid evaluation
description: and adoption in the wider ML community.
description: New techniques discovered during this work are informing the current development of the Mars
description: Sample Fetch and Return rover at Airbus as well as other machine learning groups in the space
description: industry. These tools are enabling transfer of existing techniques from the ML community into
description: the space sector. While these methods have been developed for space applications on radiation
description: hardened processors, they are equally applicable to low power terrestrial computing. The spread
description: of ML onto micro-controllers in embedded Internet of Things (IoT) devices is using these
description: techniques impacting the performance a wide range of applications outside of the space sector."