Abstract
We present three computer-augmented software engineering approaches to ensure dependability at different levels of control architectures in autonomous robots. For each approach, we outline the methodological framework, our current achievements, and open issues. Albeit our results are still preliminary, we believe that furthering research along these lines can provide cost-effective techniques to make autonomous robots safe and thus fit for commercial purposes.
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Notes
- 1.
Experiments with ESBMC are performed on an Intel Core i7-3770 quad core at 3.40 GHz with 32 GB RAM, equipped with Ubuntu 12.04 LTS 64 bit.
- 2.
Notice that most of the overall time is spent in network communication between the learner and system wrapper, in resetting the system, and other overhead tasks; less that 1 % of time was actually needed for the “learning algorithm”. All the identification experiments have been carried out on a Dell laptop with Core2Duo 2.53GHz CPU and 4GB of RAM on Ubuntu 12.04.
- 3.
This behavior is the default because in the implementation of control loops it is preferable to reduce the communication latency at the cost of dropping (late) packets.
- 4.
[0, 1] denotes a closed subinterval of \(\mathbb {R}\), i.e., every \(x \in \mathbb {R}\) such that \(0 \le x \le 1\).
- 5.
Table T can handle (i) deterministic actions, i.e., a is such that \(\forall s \in S. |T(s,a)|=1\); (ii) nondeterministic actions, i.e.,a is such that for all \(s \in S\), both \(|T(s,a)| = k\) and \(n_i=1/k\) for all \(1 \le i \le k\); and (iii) probabilistic actions, i.e., arbitrary values of \(n_i\).
- 6.
The radius \(\rho \) is not needed because we keep it fixed throughout learning and simulation. In a pure defense play, this choice does not hamper the robot’s ability to defend the goal area.
- 7.
Simulation and learning are performed on an Intel Core i5-480M quad core at 2.67 GHz with 4GB RAM, equipped with Ubuntu 12.04 LTS 64 bit.
- 8.
Verification and repair are performed on a Intel Core i3-2330M quad core at 2.20 GHz with similar RAM and OS. Verification of policies is carried out with state-of-the-art probabilistic model checkers, namely comics [29] (version 1.0), mrmc [30] (version 1.4.1), and prism [31] (version 4.0.3). All tools are run in their default configuration with the exception of comics, for which the option –concrete is selected instead of the default –abstract.
- 9.
mrmc does not implement counterexample generation, and in prism this is still a beta-stage feature.
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Cicala, G. et al. (2016). Engineering Approaches and Methods to Verify Software in Autonomous Systems. In: Menegatti, E., Michael, N., Berns, K., Yamaguchi, H. (eds) Intelligent Autonomous Systems 13. Advances in Intelligent Systems and Computing, vol 302. Springer, Cham. https://doi.org/10.1007/978-3-319-08338-4_121
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