Publications - Robot & Asset Self-Certification

The following publications have been published by the Robot & Asset Self-Certification team in the course of their ORCA Hub research to date.

Topic map grouping Robot & Asset Self-Certification publications by theme

Barnes M, Brown K, Carmona J, Cevasco D, Collu M, Crabtree C, ... Watson S. (2018). Technology Drivers in Windfarm Asset Management. Home Offshore

Cardoso RC, Ferrando A., Dennis LA., Fisher M. (2020). An Interface for Programming Verifiable Autonomous Agents in ROS. European Conference on Multi-Agent Systems (EUMAS), 2020

Chang X, Yang Y, Xiang T, Hospedales T. (2019). Disjoint Label Space Transfer Learning with Common Factorised Space. Proceedings of the AAAI Conference on Artificial Intelligence 2019, doi: 10.1609/aaai.v33i01.33013288

Dennis L, Fisher M. (2020). Verifiable Self-Aware Agent-Based Autonomous Systems. Proceedings of the IEEE, vol. 108, no. 7, pp. 1011-1026, July 2020, doi: 10.1109/JPROC.2020.2991262

Dinmohammadi F, Page V, Flynn D, Robu V, Fisher M, Patchett C, ... Webster M. (2018). Certification of Safe and Trusted Robotic Inspection of Assets. 2018 Prognostics and System Health Management Conference (PHM-Chongqing), Chongqing, 2018, pp. 276-284, doi: 10.1109/PHM-Chongqing.2018.00054

Dinmohammadi F, Flynn D, Bailey C, Pecht M, Yin C, Rajaguru P, ... Robu V. (2019). Predicting Damage and Life Expectancy of Subsea Power Cables in Offshore Renewable Energy Applications. IEEE Access, vol. 7, pp. 54658-54669, 2019, doi: 10.1109/ACCESS.2019.2911260

Farrell M, Luckcuck M, Fisher M. (2018). Robotics and Integrated Formal Methods: Necessity Meets Opportunity. Integrated Formal Methods - 14th International Conference, IFM 2018, Maynooth, Ireland, September 5-7, 2018, Proceedings (pp. 161-171). Springer International Publishing

Fisher M, Collins E, Dennis L, Luckcuck M, Webster M, Jump M, ... Zhao X. (2018). Verifiable Self-Certifying Autonomous Systems. 2018 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW), Memphis, TN, 2018, pp. 341-348, doi: 10.1109/ISSREW.2018.00028

Gao B, Yang Y, Gouk H, Hospedales T. (2020). Deep Clustering for Domain Adaptation. ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 2020, pp. 4247-4251, doi: 10.1109/ICASSP40776.2020.9053622

Gao Boyan, Yang Yongxin, Gouk Henry, Hospedales Timothy M. (2019). Deep clustering with Concrete K-Means. ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4252-4256, arXiv: 1910.08031

He, W.; Pecht, M.; Flynn, D.; Dinmohammadi, F. A Physics-Based Electrochemical Model for Lithium-Ion Battery State-of-Charge Estimation Solved by an Optimised Projection-Based Method and Moving-Window Filtering. Energies 2018, 11, 2120, doi: 10.3390/en11082120

Hernich A, Lutz C, Papacchini F, Wolter F. (2020). Dichotomies in Ontology-Mediated Querying with the Guarded Fragment. ACM Transactions on Computational Logic, (3), doi: 10.1145/3375628

Jia J, Ruan Q, Hospedales T.M. (None/Unknown). Frustratingly Easy Person Re-Identification: Generalizing Person Re-ID in Practice. British Machine Vision Conference (BMVC 2019)

Koeman V, Dennis L, Webster M, Fisher M, Hindriks K. (2020). The “Why Did You Do That?” Button: Answering Why-Questions for End Users of Robotic Systems. Engineering Multi-Agent Systems - 7th International Workshop, EMAS 2019, Montreal, QC, Canada, 2019, Revised Selected Papers (pp. 152-172). Springer International Publishing

Lee, Wei-Jen, Andoni M, Bani-Ahmed S, Flynn D, Hartmann B, Majd A, ... Rajski Parashis A. (2018). Battery storage systems: White Paper #1. IEEE, 2018, 57 p.

Li D, Yang Y, Song Y, Hospedales T. (2018). Learning to Generalize: Meta-Learning for Domain Generalization. AAAI Conference on Artificial Intelligence (AAAI 2018). Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, United States

Li Da, Zhang Jianshu, Yang Yongxin, Liu Cong, Song Yi-Zhe, Hospedales Timothy M. (2019). Episodic Training for Domain Generalization. arXiv e-prints, pp. arXiv: 1902.00113

Li J, Liu J, Yang P, Chen L, Huang X, Zhang L. (2019). Analyzing Deep Neural Networks with Symbolic Propagation: Towards Higher Precision and Faster Verification. Static Analysis - 26th International Symposium, SAS 2019, Porto, Portugal, October 8–11, 2019, Proceedings (pp. 296-319). Springer International Publishing

Li Y, Yang Y, Zhou W, Hospedales T.M. (None/Unknown). Feature-Critic Networks for Heterogeneous Domain Generalization. International Conference on Machine Learning (ICML 2019), arXiv: 1901.11448

Linker S, Papacchini F, Sevegnani M. (2020). Analysing Spatial Properties on Neighbourhood Spaces. 45th International Symposium on Mathematical Foundations of Computer Science (MFCS 2020), Prague, Czech Republic, Article No. 66; pp. 66:1–66:14, doi: 10.4230/LIPIcs.MFCS.2020.66

Luckcuck M. (2019). Why use Formal Methods for Autonomous Systems? Proceedings of the First International Workshop on Autonomous Systems Safety, Trondheim, Norway, pp. 87

Osborne M, Lantair J, Shafiq Z, Zhao X, Robu V, Flynn D, ... Perry J. (2019). UAS Operators Safety and Reliability Survey: Emerging Technologies towards the Certification of Autonomous UAS. 4th International Conference on System Reliability and Safety (ICSRS), Rome, Italy, 2019, pp. 203-212, doi: 10.1109/icsrs48664.2019.8987692

Page V, Webster M, Fisher M, Jump M. (2019).Towards a Methodology to Test UAVs in Hazardous Environments. ICAS 2019, The Fifteenth International Conference on Autonomic and Autonomous Systems, Athens, Greece, 2019

Robu V, Flynn D, Lane D. (2018). Train robots to self-certify their safe operation. Nature, 553 (7688), pp. 281. doi: 10.1038/d41586-018-00646-w

Stetco A, Dinmohammadi F, Zhao X, Robu V, Flynn D, Barnes M, ... Nenadic G. (2019). Machine learning methods for wind turbine condition monitoring: A review. Renewable Energy, 133 pp. 620-635, doi: 10.1016/j.renene.2018.10.047

Sung F, Yang Y, Zhang L, Xiang T, Torr Philip, Hospedales T. (2018). Learning to Compare: Relation Network for Few-Shot Learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 1199-1208

Tang W, Andoni M, Robu V, Flynn D. (2018). Accurately Forecasting the Health of Energy System Assets. 2018 IEEE International Symposium on Circuits and Systems (ISCAS), Florence, 2018, pp. 1-5, doi: 10.1109/ISCAS.2018.8351842

Tang W, Brown K, Flynn D, Pellae H. (2018). Integrity Analysis Inspection and Lifecycle Prediction of Subsea Power Cables. 2018 Prognostics and System Health Management Conference (PHM-Chongqing), Chongqing, 2018, pp. 105-114, doi: 10.1109/PHM-Chongqing.2018.00024

Tang W, Flynn D, Brown K, Valentin R, Zhao X. (2019). The Design of a Fusion Prognostic Model and Health Management System for Subsea Power Cables. OCEANS 2019 MTS/IEEE SEATTLE, Seattle, WA, USA, 2019, pp. 1-6, doi: 10.23919/oceans40490.2019.8962816

Tang W, Flynn D, Brown K, Valentin R, Zhao X. (2019). The Application of Machine Learning and Low Frequency Sonar for Subsea Power Cable Integrity Evaluation. OCEANS 2019 MTS/IEEE SEATTLE, Seattle, WA, USA, 2019, pp. 1-6 doi: 10.23919/oceans40490.2019.8962840

Webster M, Breza M, Dixon C, Fisher M, McCann J. (2018). Formal Verification of Synchronisation, Gossip and Environmental Effects for Wireless Sensor Networks. 18th International Workshop on Automated Verification of Critical Systems, doi: 10.29007/qb84

Webster M, Western D, Araiza-Illan D, Dixon C, Eder K, Fisher M, ... Pipe A. (2020). A corroborative approach to verification and validation of human–robot teams. The International Journal of Robotics Research, (1), doi: 10.1177/0278364919883338

Webster M, Breza M, Dixon C, Fisher M, McCann J. (2020). Exploring the effects of environmental conditions and design choices on IoT systems using formal methods. Journal of Computational Science, 45, doi: 10.1016/j.jocs.2020.101183

Yang Yongxin, Garcia Morillo Irene, Hospedales Timothy M. (2018). Deep Neural Decision Trees. Proceedings of the 2018 ICML Workshop on Human Interpretability in Machine Learning (WHI 2018), Stockholm, Sweden, arXiv e-prints, pp. arXiv: 1806.06988

Zhao X, Robu V, Flynn D, Salako K, Strigini L. (2019). Assessing the Safety and Reliability of Autonomous Vehicles from Road Testing. 2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE), Berlin, Germany, 2019, pp. 13-23, doi: 10.1109/ISSRE.2019.00012

Zhao X, Robu V, Flynn D, Dinmohammadi F, Fisher M, Webster M. (2019). Probabilistic Model Checking of Robots Deployed in Extreme Environments. Proceedings of the AAAI Conference on Artificial Intelligence, doi: 10.1609/aaai.v33i01.33018066

Zhao X, Osborne M, Lantair J, Robu V, Flynn D, Huang X, Fisher M, Papacchini F, Ferrando A. (2019). Towards Integrating Formal Verification of Autonomous Robots with Battery Prognostics and Health Management. Software Engineering and Formal Methods - 17th International Conference, SEFM 2019, Oslo, Norway, September 18–20, 2019, Proceedings (pp. 105-124). Springer International Publishing, doi: 10.1007/978-3-030-30446-1_6

Zhao X, Banks A, Sharp J, Robu V, Flynn D, Fisher M, Huang X. (2020). A Safety Framework for Critical Systems Utilising Deep Neural Networks. Computer Safety, Reliability, and Security - 39th International Conference, SAFECOMP 2020, Lisbon, Portugal, September 16–18, 2020, Proceedings (pp. 244-259). Springer International Publishing, doi: 10.1007/978-3-030-54549-9_16

Zhao X, Salako K, Strigini L, Robu V, Flynn D. (2020). Assessing Safety-Critical Systems from Operational Testing: A Study on Autonomous Vehicles. Information and Software Technology, 128, doi: 10.1016/j.infsof.2020.106393

Did you know?

A crucial output of the project will be working closely with regulators to develop standards and formal requirements for offshore RAI self-certification. This will have a wide impact on the development of formal benchmarks for robots in extreme environments, leading to a step change in the assessment of robotic research in academia.