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Online adaptation for autonomous unmanned systems driven by requirements satisfaction model

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Abstract

Autonomous unmanned systems (AUSs) emerge to replace human operators for achieving better safety, efficiency, and effectiveness in harsh and difficult missions. They usually run in a highly open and dynamic operating environment, in which some unexpected situations may occur, leading to violations of predefined requirements. In order to maintain stable performance, the AUS control software needs to predict in advance whether the requirements will be violated and then make adaptations to maximize requirements satisfaction. We propose \(\mathtt {Captain}\), a model-driven and control-based online adaptation approach, for the AUS control software. At the modeling phase, apart from the system behavior model and the operating environment model, we construct a requirements satisfaction model. At runtime, based on the requirements satisfaction model, \(\mathtt {Captain}\) first predicts whether the requirements will be violated in the upcoming situation; then identifies the unsatisfiable requirements that need to be accommodated; and finally, finds an optimal adaptation for the upcoming situation. We evaluate \(\mathtt {Captain}\) in both simulated scenarios and the real world. For the former, we use two cases of UAV Delivery and UUV Ocean Surveillance, whose results demonstrate the \(\mathtt {Captain}\) ’s robustness, scalability, and real-time performance. For the latter, we have successfully implemented \(\mathtt {Captain}\) in the DJI Matrice 100 UAV with real-world workloads.

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Notes

  1. https://github.com/YixingLuo/Captain.

  2. https://github.com/yrlu/quadrotor.

  3. https://yixingluo.github.io/Captain.github.io/.

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Acknowledgements

The work is supported in part by the National Natural Science Foundation of China under Grant Nos. 62192731 and 61751210, UK EPSRC (SAUSE), and EU H2020 Engage KTN on Drone Identity.

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Authors and Affiliations

  1. Key Laboratory of High-Confidence Software Technologies, Peking University, Ministry of Education, Beijing, China

    Yixing Luo, Haiyan Zhao & Zhi Jin

  2. School of Computer Science, Peking University, Beijing, China

    Yixing Luo, Haiyan Zhao & Zhi Jin

  3. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Yuan Zhou, Tianwei Zhang & Yang Liu

  4. School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, China

    Yang Liu

  5. School of Computing and Communications, The Open University, Milton Keynes, UK

    Danny Barthaud & Yijun Yu

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  1. Yixing Luo
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  4. Zhi Jin
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Correspondence to Zhi Jin.

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Appendix

Appendix

In this part, we illustrate the detailed requirements satisfaction functions we used in the UAV Delivery scenario and UUV surveillance scenario.

1.1 UAV Delivery scenario

  • Safety: The indicator to evaluate the safety requirement is the collision risk of UAV during the flight. Supposing that the obstacles detected by the UAV at time instant k is \(\mathcal {O}_k\), while the current state of UAV is \(s_k\). Thus, the QM of safety is \(\mathcal {X}_{S_{o}}(k) = \frac{\left\| \varvec{x}_k- \varvec{x}_o\right\| _2-r_a-r_{o}}{D_o}, \forall o \in \mathcal {O}_k\). Such that the average distance between UAV and the center of obstacle reflects the safety risk.

    $$\begin{aligned} \mathrm {DS}^2(\mathcal {X}_{S_{o}}(k))=\left\{ \begin{aligned}&1 ,&\mathcal {X}_{S_{o}}(k) \ge 1 \\&0,&\mathcal {X}_{S_{o}}(k) < 0 \\&\mathcal {X}_{S_{o}}(k),&\mathrm{otherwise} \end{aligned} \right. \end{aligned}$$

  • Timelines: The total traveling time from time instant i to j is denoted as \(\xi _{ij} = \sum _{k=i}^{j-1} \frac{\left\| \varvec{x}_{k+1}-\varvec{x}_k \right\| _2}{\varvec{v}_k}\). The indicator of timeliness is \(\mathcal {X}_{\xi }(T) = \xi _{0T}\), the degree of satisfaction of the timeliness requirement of the whole trajectory is:

    $$\begin{aligned} \mathrm {DS}^1(\mathcal {X}_{\xi }(T))=\left\{ \begin{aligned}&1 ,&\mathcal {X}_{\xi }(T) \le \varDelta _t \\&\frac{\varDelta - \mathcal {X}_{\xi }(T)}{\varDelta -\varDelta _t},&\varDelta _t <\mathcal {X}_{\xi }(T) \le \varDelta \\&0,&\mathcal {X}_{\xi }(T) > \varDelta \end{aligned} \right. \end{aligned}$$

  • Accuracy: The average quality of the information collected during the mission is denoted as \(\mathcal {X}_{\varphi }(T) = \frac{1}{\xi _{0T}}\sum _{k=0}^{T-1}\left\| \varvec{\omega }\right\| \tau \), the degree of satisfaction is \(DS_{\varphi }\) is:

    $$\begin{aligned} \mathrm {DS}^2(\mathcal {X}_{\varphi }(T))=\left\{ \begin{aligned}&1 ,&\mathcal {X}_{\varphi }(T)\ge A_t \\&\frac{\mathcal {X}_{\varphi }(T)-A}{A_t-A},&A \le \mathcal {X}_{\varphi }(T)< A_t \\&0,&\mathcal {X}_{\varphi }(T) < A \end{aligned} \right. \end{aligned}$$

  • Energy-saving: The total energy consumption from time instant i to j is denoted as \(e_{ij} =\sum _{k=i}^{j-1} \left\| \varvec{x}_{k+1}-\varvec{x}_k\right\| _2 + \eta _1 \cdot \left\| \varvec{v}_{k+1}-\varvec{v}_k\right\| _2 + \eta _2 \cdot \left\| \varvec{\omega }_k\right\| \tau \). The indicator of energy consumption is \(\mathcal {X}_E(T) = e_{0T}\), the degree of satisfaction of energy requirement \(DS_{e}\) is:

    $$\begin{aligned} \mathrm {DS}^1(\mathcal {X}_{E}(T))=\left\{ \begin{aligned}&1 ,&\mathcal {X}_E(T) \le E_t \\&\frac{E-\mathcal {X}_E(T)}{E-E_t},&E_t < \mathcal {X}_E(T) \le E \\&0,&\mathcal {X}_E(T) > E \end{aligned} \right. \end{aligned}$$

1.2 UUV surveillance scenario

The UUV is equipped with 5 sensors for ocean surveillance. The scanning time 10 hours is 360 time instance, \(x_i, i\in [1,5]\) is the portion of time the sensor i should be used during system operation in each instance. \(Acc_i\) is the accuracy of sensor i; \(E_i\) is the energy consumed by sensor; \(V_i\) is the scanning speed of sensor. \(q_i\) is portion of accuracy of sensor and \(p_i\) is for scanning speed, respectively, in decimals. The energy consumed is related with working accuracy and speed of sensor. The corresponding measures are listed as follows: \(\mathcal {X}_{L}(T)= \sum _{k=0}^{T} \sum _{i=0}^{N} x_iq_iV_i \tau \), \(\mathcal {X}_{E}(T)= \sum _{k=0}^{T} \sum _{i=0}^{N} x_iE_i\cdot \frac{e^{p_i + q_i}-1}{e^2-1} \tau \), and \(\mathcal {X}_{\varphi }(T)=\sum _{k=0}^{T} \sum _{i=0}^{N} x_ip_iAcc_i\), where \(T=360\), i.e., adaptations are performed every 100 surface measurements of the UUV state, and the time instance k incremented by \(1\sim 100\). The requirements satisfaction functions are listed as follows:

  • Scanning distance: A segment of surface over a distance of \(L_t=100\) km is expected to be examined by the UUV within \(\varDelta =10\) hours, while the distance threshold is \(L=90\) km.

    $$\begin{aligned} \mathrm {DS}^2(\mathcal {X}_{L}(T))=\left\{ \begin{aligned}&1 ,&\mathcal {X}_{L}(T)\ge L_t \\&\frac{\mathcal {X}_{L}(T)-L}{L_t-L},&L \le \mathcal {X}_{L}(T)< L_t \\&0,&\mathcal {X}_{L}(T) < L \end{aligned} \right. \end{aligned}$$

  • Energy-saving: A total amount of energy \(E_t=5.4\) MJ is expected to be consumed, while the maximum amount of energy is \(E=6\) MJ.

    $$\begin{aligned} \mathrm {DS}^1(\mathcal {X}_{E}(T))=\left\{ \begin{aligned}&1 ,&\mathcal {X}_E(T) \le E_t \\&\frac{E-\mathcal {X}_E(T)}{E-E_t},&E_t < \mathcal {X}_E(T) \le E \\&0,&\mathcal {X}_E(T) > E \end{aligned} \right. \end{aligned}$$

  • Accuracy: The accuracy of sensor measurements is targeted at \(A_t=90\%\), while the accuracy threshold is set as \(A = 80\%\).

    $$\begin{aligned} \mathrm {DS}^2(\mathcal {X}_{\varphi }(T))=\left\{ \begin{aligned}&1 ,&\mathcal {X}_{\varphi }(T)\ge A_t \\&\frac{\mathcal {X}_{\varphi }(T)-A}{A_t-A},&A \le \mathcal {X}_{\varphi }(T)< A_t \\&0,&\mathcal {X}_{\varphi }(T) < A \end{aligned} \right. \end{aligned}$$

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Luo, Y., Zhou, Y., Zhao, H. et al. Online adaptation for autonomous unmanned systems driven by requirements satisfaction model. Softw Syst Model 21, 1295–1319 (2022). https://doi.org/10.1007/s10270-022-00981-7

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