Probabilistic modelling and safety assurance of an agriculture robot providing light-treatment

TY - GEN

T1 - Probabilistic modelling and safety assurance of an agriculture robot providing light-treatment

AU - Adam, Mustafa

AU - Ye, Kangfeng

AU - Anisi, David A.

AU - Cavalcanti, Ana

AU - Woodcock, Jim

AU - Morris, Robert

N1 - Funding Information: The authors would like to gratefully acknowledge all guidance and fruitful discussions provided by Robert Morris on the hazard and safety aspects of agricultural robots. The research presented in this paper has received partial funding from the Norwegian Research Council (RCN) Robo-Farmer, project number 336712, the UK EPSRC Grants EP/M025756/1, EP/R025479/1, and EP/V026801/2, and the Royal Academy of Engineering Grant No CiET1718/45. Publisher Copyright: © 2023 IEEE.

PY - 2023/9/28

Y1 - 2023/9/28

N2 - 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 offwith 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 adoptinga 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.

AB - 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 offwith 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 adoptinga 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.

UR - http://www.scopus.com/inward/record.url?scp=85174405784&partnerID=8YFLogxK

U2 - 10.1109/CASE56687.2023.10260395

DO - 10.1109/CASE56687.2023.10260395

M3 - Conference contribution

AN - SCOPUS:85174405784

SN - 979-8-3503-2070-1

T3 - IEEE International Conference on Automation Science and Engineering

BT - 2023 IEEE 19th International Conference on Automation Science and Engineering, CASE 2023

PB - IEEE

T2 - 19th IEEE International Conference on Automation Science and Engineering, CASE 2023

Y2 - 26 August 2023 through 30 August 2023

ER -