Abstract
The battery is a key component of autonomous robots. Its performance limits the robot’s safety and reliability. Unlike liquid-fuel, a battery, as a chemical device, exhibits complicated features, including (i) capacity fade over successive recharges and (ii) increasing discharge rate as the state of charge (SOC) goes down for a given power demand. Existing formal verification studies of autonomous robots, when considering energy constraints, formalise the energy component in a generic manner such that the battery features are overlooked. In this paper, we model an unmanned aerial vehicle (UAV) inspection mission on a wind farm and via probabilistic model checking in PRISM show (i) how the battery features may affect the verification results significantly in practical cases; and (ii) how the battery features, together with dynamic environments and battery safety strategies, jointly affect the verification results. Potential solutions to explicitly integrate battery prognostics and health management (PHM) with formal verification of autonomous robots are also discussed to motivate future work.
Supported by the UK EPSRC through the Offshore Robotics for Certification of Assets (ORCA) [EP/R026173/1], Robotics and Artificial Intelligence for Nuclear (RAIN) [EP/R026084] and Science of Sensor System Software (S4) [EP/N007565].
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Notes
- 1.
We have used the word “drone” interchangeably with the abbreviation UAV as a less formal naming convention throughout the paper.
- 2.
- 3.
- 4.
- 5.
Since we focus on the particular failure mode of out-off-battery in our model, rigorously this should be the probability of seeing no out-off-battery failures in a mission.
- 6.
It is a first approximation in the sense of, e.g. the simplification of two levels of wind speed and the round estimations of battery consumption in Table 1.
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Zhao, X. et al. (2019). Towards Integrating Formal Verification of Autonomous Robots with Battery Prognostics and Health Management. In: Ölveczky, P., Salaün, G. (eds) Software Engineering and Formal Methods. SEFM 2019. Lecture Notes in Computer Science(), vol 11724. Springer, Cham. https://doi.org/10.1007/978-3-030-30446-1_6
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