Autonomous science for an ExoMars Rover–like mission

Woods, Mark; Shaw, Andy; Barnes, Dave; Price, D. C.; Long, Derek; Pullan, D.

doi:10.1002/rob.20289

Cited by 44 publications

(32 citation statements)

References 23 publications

(21 reference statements)

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“…Opportunistic plan fragments are computed offline, and executed online if their resource requirements are met. Woods et al [41] assume that the types of opportunities that can arise are known, and that all opportunities of the same type can be exploited by the same opportunistic plan fragment. Plan fragments are precomputed and stored in a plan library.…”

Section: Relationship To Other Probabilistic Modelsmentioning

confidence: 99%

Opportunistic Planning in Autonomous Underwater Missions

Cashmore

Fox

Long

et al. 2018

IEEE Trans. Automat. Sci. Eng.

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Abstract-This paper explores the execution of planned AUV missions where opportunities to achieve additional utility can arise during execution. The missions are represented as temporal planning problems, with hard goals and time constraints. Opportunities are soft goals with high utility. The probability distributions for the occurrences of these opportunities are not known, but it is known that they are unlikely so it is not worth trying to anticipate their occurrence prior to plan execution. However, as they are high utility, it is worth trying to address them dynamically when they are encountered, as long as this can be done without sacrificing the achievement of the hard goals of the problem. We formally characterise the opportunistic planning problem, introduce a novel approach to opportunistic planning and compare it to an on-board replanning approach in the domain of autonomous underwater vehicles performing pillar expection and chain following tasks.Note to Practitioners-This paper concerns high level intelligent automation of unmanned vehicle operations in the context of undersea inspection and maintenance. The objective is to provide a robust long-term autonomy, enabling the vehicle to make its own decisions about how to prioritise goals and use its resources. Plans to achieve large numbers of goals over time are constructed autonomously by a planning system using models of activity and resource consumption. In order to avoid running up against resource bounds in a way that would compromise robustness, models of resource consumption are conservative. An important aspect of long-term autonomy concerns how unused resources, that accumulate over time because of conservative assumptions, can be used to increase overall utility. The approach we describe is deterministic: we do not model uncertainty or allow the planner to reason with contingencies. Instead, we focus on how to exploit resource intelligently to obtain the best available utility, in a way that does not undermine the reliability or predictability of operational behaviour.

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Section: Relationship To Other Probabilistic Modelsmentioning

confidence: 99%

Opportunistic Planning in Autonomous Underwater Missions

Cashmore

Fox

Long

et al. 2018

IEEE Trans. Automat. Sci. Eng.

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“…Examples include the MER software architecture described in Refs [17,18], the CREST software architecture for an ExoMars-like rover [19], and even the latest MSL rover except that it has modular executors [20] that may operate at a different LoA or adaptability though still are limited to E3.…”

Section: Onboard Operation Softwarementioning

confidence: 99%

Mission Operations and Autonomy

Gao

Burroughes

Ocón

et al. 2016

Contemporary Planetary Robotics

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“…The research presented here builds upon previous work [3] funded by the UK STFC. This work demonstrated autonomous science target identification and rover arm placement within our Trans-National Planetary Analogue Terrain Laboratory (PATLab).…”

Section: Introductionmentioning

confidence: 95%

Autonomous Science Target Identification and Acquisition (ASTIA) for planetary exploration

Barnes

Pugh

Tyler

2009

2009 IEEE/RSJ International Conference on Intelligent Robots and Systems

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We introduce an autonomous planetary exploration software architecture being developed for the purpose of autonomous science target identification and surface sample acquisition. Our motivation is to maximise planetary science data return whilst minimising the need for ground-based human intervention during long duration planetary robotic exploration missions. Our Autonomous Science Target Identification and Acquisition (ASTIA) architecture incorporates a number of key software components which support 2D and 3D image processing; autonomous science target identification based upon science instrument captured data; a robot manipulator control software agent, and an architecture software executive. ASTIA is being developed and tested within our Trans-National Planetary Analogue Terrain Laboratory (PATLab). This provides an analogue Martian terrain, and a rover chassis with onboard manipulator, cameras and computing hardware. Experimentation results with ASTIA and our PATLab rover are presented.

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Autonomous science for an ExoMars Rover–like mission

Cited by 44 publications

References 23 publications

Opportunistic Planning in Autonomous Underwater Missions

Opportunistic Planning in Autonomous Underwater Missions

Mission Operations and Autonomy

Autonomous Science Target Identification and Acquisition (ASTIA) for planetary exploration

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