Alireza David Anisi

• Adam, Mustafa; Ye, Kangfeng; Anisi, Alireza David; Cavalcanti, Ana; Woodcock, Jim & Morris, Robert (2023). Probabilistic Modelling and Safety Assurance of an Agriculture Robot Providing Light-Treatment, Proceedings of the 2023 19th IEEE International Conference on Automation Science and Engineering (CASE) . IEEE conference proceedings. ISSN 979-8-3503-2070-1. p. 1–7. doi: 10.1109/CASE56687.2023.10260395. Show summary

  Continued adoption of agricultural robots postulates the farmer's trust in the reliability, robustness and safety of the new technology. This motivates our work on safety assurance of agricultural robots, particularly their ability to detect, track and avoid obstacles and humans. This paper considers a probabilistic modelling and risk analysis framework for use in the early development phases. Starting off with hazard identification and a risk assessment matrix, the behaviour of the mobile robot platform, sensor and perception system, and any humans present are captured using three state machines. An auto-generated probabilistic model is then solved and analysed using the probabilistic model checker PRISM. The result provides unique insight into fundamental development and engineering aspects by quantifying the effect of the risk mitigation actions and risk reduction associated with distinct design concepts. These include implications of adopting a higher performance and more expensive Object Detection System or opting for a more elaborate warning system to increase human awareness. Although this paper mainly focuses on the initial concept-development phase, the proposed safety-assurance framework can also be used during implementation, and subsequent deployment and operation phases.

• Serbezov, Atanas; Rhinehart, Russ; Goupil, Philippe & Anisi, Alireza David (2022). Academic-Practice Collaborations in Automation and Control: Keys for Success. IFAC-PapersOnLine. ISSN 2405-8963. 55(17), p. 308–313. doi: 10.1016/j.ifacol.2022.09.297. Show summary

  This study identifies the keys for successful collaboration between the communities of academia and practice in the field of automation and control. The findings are based on the analysis of 85 different collaborations reported in a survey of the international control community conducted by the Industry and Education Committees at IFAC. Addressing a joint problem, understanding and respecting each other, frequent communication, and adequate funding are among the top attributes of successful collaboration. A complete list of the top 10 keys for success is provided in the conclusion section.

• Murray, Yvonne; Sirevåg, Martin; Ribeiro, Pedro; Anisi, Alireza David & Mossige, Morten (2022). Safety assurance of an industrial robotic control system using hardware/software co-verification. Science of Computer Programming. ISSN 0167-6423. 216. doi: 10.1016/j.scico.2021.102766. Full text in Research Archive Show summary

  As a general trend in industrial robotics, an increasing number of safety functions are being developed or re-engineered to be handled in software rather than by physical hardware such as safety relays or interlock circuits. This trend reinforces the importance of supplementing traditional, input-based testing and quality procedures which are widely used in industry today, with formal verification and model-checking methods. To this end, this paper focuses on a representative safety-critical system in an ABB industrial paint robot, namely the High-Voltage electrostatic Control system (HVC). The practical convergence of the high-voltage produced by the HVC, essential for safe operation, is formally verified using a novel and general co-verification framework where hardware and software models are related via platform mappings. This approach enables the pragmatic combination of highly diverse and specialised tools. The paper's main contribution includes details on how hardware abstraction and verification results can be transferred between tools in order to verify system-level safety properties. It is noteworthy that the HVC application considered in this paper has a rather generic form of a feedback controller. Hence, the co-verification framework and experiences reported here are also highly relevant for any cyber-physical system tracking a setpoint reference.

• Murray, Yvonne; David_Anisi, Alireza; Sirevåg, Martin; Ribeiro, Pedro & hagag, Rabah Saleh (2020). Safety Assurance of a High Voltage Controller for an Industrial Robotic System. In Carvalho, Gustavo & Stolz, Volker (Ed.), Formal Methods: Foundations and Applications. Springer. ISSN 978-3-030-63882-5. p. 45–63. doi: 10.1007/978-3-030-63882-5_4. Full text in Research Archive
• Murray, Yvonne & Anisi, Alireza David (2019). Survey of Formal Verification Methods for Smart Contracts on Blockchain. IFIP International Conference on New Technologies, Mobility and Security (NTMS). ISSN 2157-4952. doi: 10.1109/NTMS.2019.8763832.
• Espeland, Elene Marie; Jung, Byung Kyu & Anisi, Alireza David (2019). A predictive learning approach to optimal load sharing in energy management systems. In Garofalo, Franco (Eds.), 2019 18th European Control Conference (ECC). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-3-907144-00-8. p. 498–504. doi: 10.23919/ECC.2019.8796043.
• Anisi, Alireza David; Tveide, Svein Gjermund & Kongezos, Valentinos (2017). State classification for autonomous gas sample taking using deep convolutional neural networks. In Koutsoukos, Xenofon & Bugeja, Marvin (Ed.), 25th Mediterranean Conference on Control and Automation. IEEE conference proceedings. ISSN 978-1-5090-4533-4. p. 370–375. doi: 10.1109/med.2017.7984146.
• Kaldestad, Knut Berg; Haddadin, Sami; Belder, Rico; Hovland, Geir & Anisi, Alireza David (2014). Collision Avoidance with Potential Fields Based on Parallel Processing of 3D-Point Cloud Data on the GPU, 2014 IEEE International Conference on Robotics and Automation (ICRA 2014). Curran Associates, Inc.. ISSN 9781479936861. p. 3250–3257. doi: 10.1109/icra.2014.6907326.

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• David_Anisi, Alireza; Kongezos, Valentinos; Krieger, Roland; Lillehagen, Tommy & Skourup, Charlotte (2013). Robot-human cooperation for error recovery in oil and gas robotic applications.

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