Assurance for Autonomy - JPL's past research, lessons learned, and future directions

被引:1
作者
Feather, Martin S. [1 ]
Pinto, Alessandro [1 ]
机构
[1] CALTECH, Jet Prop Lab, Off Safety & Mission Success, Pasadena, CA 91125 USA
来源
2023 IEEE INTERNATIONAL CONFERENCE ON ASSURED AUTONOMY, ICAA | 2023年
基金
美国国家航空航天局;
关键词
assurance; autonomy; testing; validation; verification; SAFETY;
D O I
10.1109/ICAA58325.2023.00022
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Robotic space missions have long depended on automation, defined in the 2015 NASA Technology Roadmaps as "the automatically-controlled operation of an apparatus, process, or system using a pre-planned set of instructions (e.g., a command sequence)," to react to events when a rapid response is required. Autonomy, defined there as "the capacity of a system to achieve goals while operating independently from external control," is required when a wide variation in circumstances precludes responses being pre-planned, instead autonomy follows an on-board deliberative process to determine the situation, decide the response, and manage its execution. Autonomy is increasingly called for to support adventurous space mission concepts, as an enabling capability or as a significant enhancer of the science value that those missions can return. But if autonomy is to be allowed to control these missions' expensive assets, all parties in the lifetime of a mission, from proposers through ground control, must have high confidence that autonomy will perform as intended to keep the asset safe to (if possible) accomplish the mission objectives. The role of mission assurance is a key contributor to providing this confidence, yet assurance practices honed over decades of spaceflight have relatively little experience with autonomy. To remedy this situation, researchers in JPL's software assurance group have been involved in the development of techniques specific to the assurance of autonomy. This paper summarizes over two decades of this research, and offers a vision of where further work is needed to address open issues.
引用
收藏
页码:97 / 105
页数:9
相关论文
共 66 条
  • [1] [Anonymous], 2012, Soil Moisture Active Passive Mission
  • [2] [Anonymous], 2015, NASA TECHNOLOGY ROAD
  • [3] [Anonymous], 2016, NASA STD 7009A WCHAN
  • [4] Ballin Mark, 2016, NASA ARMD STRATEGIC
  • [5] Bernard DE, 1998, 1998 IEEE AEROSPACE CONFERENCE PROCEEDINGS, VOL 2, P259, DOI 10.1109/AERO.1998.687914
  • [6] Castano R., 2005, LEARNING CLASSIFIERS
  • [7] Formal Methods for Trusted Space Autonomy: Boon or Bane?
    Chien, Steve A.
    [J]. NASA FORMAL METHODS (NFM 2022), 2022, 13260 : 3 - 13
  • [8] Doyle R., 2016, VALIDATION AUTONOMOU
  • [9] Compositional Falsification of Cyber-Physical Systems with Machine Learning Components
    Dreossi, Tommaso
    Donze, Alexandre
    Seshia, Sanjit A.
    [J]. JOURNAL OF AUTOMATED REASONING, 2019, 63 (04) : 1031 - 1053
  • [10] Drusinsky D, 2004, 28TH ANNUAL NASA GODDARD SOFTWARE ENGINEERING WORKSHOP, PROCEEDINGS, P127