Skip to main content
Log in

Decentralized navigation method for a robotic swarm with nonhomogeneous abilities

  • Published:
Autonomous Robots Aims and scope Submit manuscript

Abstract

This paper addresses the navigation of a robotic swarm with nonhomogeneous abilities, including sensing range, maximum velocity, and acceleration. With this method, the robotic swarm moves in a two-dimensional plane, and each follower distributedly constructs and maintains local directed connection using only local information to achieve maintenance of global connectivity. We also ensure the swarm is stable when the leader moves at a constant velocity. Validity and effectiveness of the proposed control strategy are shown by theoretical analysis, experiments with real robots, and numerical simulations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22

Similar content being viewed by others

References

  • Alligood, K. T., Sauer, T. D., & Yorke, J. A. (1996). Chaos: An introduction to dynamical systems. New York: Springer.

    MATH  Google Scholar 

  • Ardito, C. F., Paola, D. D., & Gasparro, A. (2012). Decentraized estimation of the minimum strongly connected subdigraph for robotic networks with limited field of view. In Proceedings of 51st IEEE CDC (pp. 5304–5309).

  • Brambilla, M., Ferrante, E., Birattari, M., & Dorigo, M. (2013). Swarm robotics: A review from the swarm engineering perspective. Swarm Intelligence, 7(1), 1–41.

    Article  Google Scholar 

  • Bullo, F., Cortes, J., & Martinez, S. (2009). Distributed control of robotic networks: A mathematical approach to motion coordination algorithms. Princeton: Princeton Univ Press.

    Book  Google Scholar 

  • Cezayirli, A., & Kerestecioglu, F. (2013). Navigation of non-communicating autonomous mobile robots with guaranteed connectivity. Robotica, 31(5), 767–776.

    Article  Google Scholar 

  • Dimarogonas, D. V., & Johansson, K. H. (2010). Bounded control of network connectivity in multi-agent systems. IET Control Theory and Applications, 4(8), 1330–1338.

    Article  MathSciNet  Google Scholar 

  • Durham, J. W., Franchi, A., & Bullo, F. (2012). Distributed pursuit-evasion without mapping or global localization via local frontiers. Autonomous Robots, 32, 81–95.

    Article  Google Scholar 

  • Fang, H., Wei, Y., Chen, J., & Xin, B. (2017). Flocking of second-order multiagent systems with connectivity preservation based on algebraic connectivity estimation. IEEE Transactions on Cybernetics, 47(4), 1067–1077.

    Article  Google Scholar 

  • Feng, Y., Xu, S., Lewis, F. L., & Zhang, B. (2015). Consensus of heterogeneous first- and second-order multi-agent systems with directed communication topologies. International Journal of Robust Nonlinear Control, 25(3), 362–375.

    Article  MathSciNet  Google Scholar 

  • Feng, Z., Sun, C., & Hu, G. (2017). Robust connectivity preserving rendezvous of multi-robot systems under unknown dynamics and disturbances. IEEE Transactions on Control of Network Systems, 4(4), 725–735.

    Article  MathSciNet  Google Scholar 

  • Gasparri, A., Priolo, A., & Ulivi, G. (2012) A swarm aggregation algorithm for multi-robot systems based on local interaction. In Proceedings of 2012 IEEE multi-conference on systems and control (pp. 1497–1502).

  • Howard, A., Parker, L. E., & Sukhatme, G. S. (2006). Experiments with a large heterogeneous mobile robot team: Exploration mapping, deployment and detection. The International Journal of Robotics Research, 25, 431–447.

    Article  Google Scholar 

  • Ji, M., & Egerstedt, M. (2007). Distributed coordination control of multiagent systems while preserving connectedness. IEEE Transactions on Automatic Control, 23(4), 693–703.

    Google Scholar 

  • Kawakami, H., & Namerikawa, T. (2009). Cooperative target-capturing strategy for multi-vehicle systems with dynamic network topology. In Proceedings of 2009 American control conference (pp. 635–640).

  • Li, Y., & Muldowney, J. S. (1993). On Bendixson’s criterion. Journal of Differential Equations, 106, 27–39.

    Article  MathSciNet  Google Scholar 

  • Mei, J., Ren, W., & Chen, J. (2016). Distributed consensus of second-order multi-agent systems with heterogeneous unknown inertias and control gains under a directed graph. IEEE Transactions on Automactic Control, 61(8), 2019–2034.

    Article  MathSciNet  Google Scholar 

  • Miyata, N., Ota, J., Arai, T., & Asama, H. (2002). Cooperative transport by multiple mobile robots in unknown static environments associated with real-time task assignment. IEEE Transactions on Robotics Automation, 18(5), 769–780.

    Article  Google Scholar 

  • Navarro, I., & Matia, F. (2013). A survey of collective movement of mobile robots. International Journal of Advanced Robotic Systems, 10(73), 1–9.

    Google Scholar 

  • Olfati-Saber, R. (2006). Flocking for multi-agent dynamic systems: Algorithms and theory. IEEE Transactions on Automatic Control, 51(3), 401–420.

    Article  MathSciNet  Google Scholar 

  • Ota, J. (2006). Multi-agent robot systems as distributed autonomous systems. Advanved Engineering Informatics, 20(1), 59–70.

    Article  Google Scholar 

  • Panagou, D., Stipanovic, D. M., & Voulgaris, P. G. (2016). Distributed coordination control for multi-robot networks using Lyapunov-like barrier functions. IEEE Transactions on Automatic Control, 61(3), 617–632.

    Article  MathSciNet  Google Scholar 

  • Paola, D. D., Asmundis, R. D., Gasparri, A., & Rizzo, A. (2012). Decentralized topology control for robotic network with limited field of view sensors. In Proceedings of 2012 American Control Conference (pp. 3167–3172).

  • Qu, Z., Li, C., & Lewis, F. (2014). Cooperative control with distributed gain adaptation and connectivity estimation for directed networks. International Journal of Robust Nonlinear Control, 24, 450–476.

    Article  MathSciNet  Google Scholar 

  • Ren, W., & Beard, R. W. (2005). Consensus seeking in multiagent systems under dynamically changing interaction topologies. IEEE Transactions on Automatic Control, 50(5), 655–661.

    Article  MathSciNet  Google Scholar 

  • Sabattini, L., Secchi, C., & Chopra, N. (2015). Decentralized estimation and control for preserving the strong connectivity of directed graphs. IEEE Transactions on Cybernetics, 45(10), 2273–2286.

    Article  Google Scholar 

  • Schuresko, M., & Cortes, J. (2012). Distributed tree rearrangement for reachability and robust connectivity. SIAM Journal on Control and Optimization, 50(5), 2588–2620.

    Article  MathSciNet  Google Scholar 

  • Shi, G., Hong, Y., & Johansson, K. H. (2012). Connectivity and set tracking of multi-agent systems guided by multiple moving leaders. IEEE Transactions on Automatic Control, 57(3), 663–676.

    Article  MathSciNet  Google Scholar 

  • Sontag, E. D. (2003). A remark on the converging-input converging-state property. IEEE Transactions on Automatic Control, 48(2), 313–314.

    Article  MathSciNet  Google Scholar 

  • Verginis, C. K., Bechlioulis, C. P., Dimarogonas, D. V., & Kyriakopoulos, K. J. (2015). Decentralized 2-D control of vehicular platoons under limited visual feedback. In Proceedings of 2015 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 3566–3571).

  • Zavlanos, M. M., Egerstedt, M. B., & Pappas, G. J. (2011). Graph-theoretic connectivity control of mobile robot networks. Proceedings of the IEEE, 99(9), 1525–1540.

    Article  Google Scholar 

  • Zheng, Y., Zhu, Y., & Wang, L. (2011). Consensus of heterogeneous multi-agent systems. IET Control Theory and Applications, 5(16), 1881–1888.

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Takahiro Endo.

Additional information

This is one of several papers published in Autonomous Robots comprising the “Special Issue on Distributed Robotics: From Fundamentals to Applications”.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (mp4 6673 KB)

Appendices

Appendix A: Proof of 2nd step in Theorem 2

Now, we show that \(\Vert \dot{\varvec{u}}_i(t) \Vert < A_i\) if \(\Vert \varvec{u}_{j}(t) \Vert \le U_{n+1}\). Here, we consider \(t \ge t_i\) because \(\dot{\varvec{u}}_i \equiv \varvec{0}\) at \(t < t_i\). Substituting (23) into the time-derivative of (8), the acceleration of agent i is computed as

$$\begin{aligned} \dot{\varvec{u}}_i = \left( \dot{u}_{ir} - u_{i\theta }\omega \right) \varvec{e}_r + \left( \dot{u}_{i\theta } + u_{ir}\omega \right) \varvec{e}_\theta , \end{aligned}$$
(31)

where \(\omega \) is defined in (22). From (31), the norm of the acceleration of agent i is calculated as

$$\begin{aligned} \Vert \dot{\varvec{u}}_i \Vert ^2 =\ (\dot{u}_{ir}^2 + \dot{u}_{i\theta }^2) + \omega ^2(u_{ir}^2 + u_{i\theta }^2) + 2\omega (u_{ir}\dot{u}_{i\theta } - u_{i\theta }\dot{u}_{ir}). \end{aligned}$$
(32)

(case 1: If \(0 \le r < \rho ')\) Obviously \(\Vert \dot{\varvec{u}}_i \Vert = 0 < A_i\).

(case 2: If \(\rho ' \le r < r_c)\) From (10),

$$\begin{aligned} \dot{u}_{ir}&= a\dot{r} = a(u_{jr} - u_{ir}), \end{aligned}$$
(33)
$$\begin{aligned} \dot{u}_{i\theta }&= \sigma \dot{u}_{ir} = \sigma a (u_{jr} - u_{ir}). \end{aligned}$$
(34)

Substituting (10), (22), (33), (34) and \(\sigma ^2 \le 1\) into (32), we have

$$\begin{aligned} \Vert \dot{\varvec{u}}_i \Vert ^2 < 2a^2&\left\{ \left( u_{jr} - u_{ir}\right) ^2 + \left( u_{j\theta } - u_{i\theta }\right) ^2 \right\} \\ = 2a^2&\bigl \{ \left( u_{jr}^2 + u_{j\theta }^2\right) + \left( 1 +\sigma ^2 \right) u_{ir}^2 \\&-2u_{ir}\left( u_{jr} + \sigma u_{j\theta } \right) \bigr \} \\ \le 2a^2&\left\{ \left( u_{jr}^2+ u_{j\theta }^2\right) + 2u_{ir}^2 + 2u_{ir} \left( \vert u_{jr} \vert + \vert u_{j\theta } \vert \right) \right\} . \end{aligned}$$

This inequality yields

$$\begin{aligned} \Vert \dot{\varvec{u}}_i \Vert ^2 < (1 + \sqrt{2})^2 a^2 U_{n+1}^2, \end{aligned}$$
(35)

since \(u_{jr}^2 + u_{j\theta }^2 \le U_{n+1}^2\) , \(\vert u_{jr} \vert + \vert u_{j\theta } \vert \le \sqrt{2}U_{n+1}\) using the method of Lagrange multiplier, and \(u_{ir} < U' / 2\) from (20). Thus, substituting (7) into (35), we obtain

$$\begin{aligned} \Vert \dot{\varvec{u}}_i \Vert< & {} \left( 1 + \sqrt{2} \right) a U_{n+1} \le \frac{1 + \sqrt{2}}{2 + \sqrt{2}}a \min _{k \in \mathcal {F}}\frac{A_k}{a_k}\\\le & {} \frac{1 + \sqrt{2}}{2 + \sqrt{2}}a\frac{A_i}{a} < A_i. \end{aligned}$$

(case 3: If \(r_c \le r \le r_e)\) In this interval of r,

$$\begin{aligned} \dot{u}_{ir}&= a\dot{r} = a(u_{jr} - u_{ir}),\end{aligned}$$
(36)
$$\begin{aligned} \dot{u}_{i\theta }&= -\sigma a (u_{jr} - u_{ir}) \end{aligned}$$
(37)

from (11). Substituting (22), (36), (37) and \(\sigma ^2 \le 1\) into (32), we have

$$\begin{aligned} \Vert \dot{\varvec{u}}_i \Vert ^2 =&\ (1 + \sigma ^2)a^2 (u_{jr} - u_{ir})^2 \\&- \frac{2a}{r}(u_{jr} - u_{ir})(u_{j\theta } - u_{i\theta })(\sigma u_{ir} + u_{i\theta })\\&+ \frac{1}{r^2}(u_{j\theta } - u_{i\theta })^2 (u_{ir}^2 + u_{i\theta }^2). \end{aligned}$$

Since \(r = \rho ' + u_{ir}/a > u_{ir}/a\) from (11), \(u_{ir}\ge u_{i\theta }\), and \(\sigma ^2 \le 1\),

$$\begin{aligned} \Vert \dot{\varvec{u}}_i \Vert ^2 <&\ 2a^2 \{ (u_{jr} - u_{ir})^2 \\&+2\vert u_{jr} - u_{ir} \vert \vert u_{j\theta } - u_{i\theta } \vert + (u_{j\theta } - u_{i\theta })^2 \} \\ =&\ 2a^2(\vert u_{jr} - u_{ir} \vert + \vert u_{j\theta } - u_{i\theta } \vert )^2 \\ \le&\ 2a^2(\vert u_{ir} \vert + \vert u_{i\theta } \vert + \vert u_{jr} \vert + \vert u_{j\theta } \vert )^2. \end{aligned}$$

Substituting \(\vert u_{ir} \vert + \vert u_{i\theta } \vert \le U' < U_{n+1}\) from (11), and \(\vert u_{jr} \vert + \vert u_{j\theta } \vert \le \sqrt{2}U_{n+1}\) as the second case to (32), \(\Vert \dot{\varvec{u}}_i \Vert ^2 < (2 + \sqrt{2})^2 a^2 U_{n+1}^2\). Thus, substituting (7) into (35), we obtain

$$\begin{aligned} \Vert \dot{\varvec{u}}_i \Vert< & {} (2 + \sqrt{2})aU_{n+1} \le \frac{2+\sqrt{2}}{2+\sqrt{2}}a \min _{k \in \mathcal {F}}\frac{A_k}{a_k}\\\le & {} \frac{2+\sqrt{2}}{2+\sqrt{2}}a \frac{A_i}{a} = A_i. \end{aligned}$$

Thus, \(\Vert \dot{\varvec{u}}_i \Vert \) is smaller than \(A_i\).

Appendix B: Proof of Lemma 1 (existence of the equilibrium)

(case 1: If \(\rho '< r^\mathrm {eq} < r_c)\)The equilibrium satisfies

$$\begin{aligned}&U^* \cos \theta ^\mathrm {eq} - a(r^\mathrm {eq} - \rho ') = 0,\end{aligned}$$
(38)
$$\begin{aligned}&\sigma a(r^\mathrm {eq} - \rho ') - U^* \sin \theta ^\mathrm {eq} = 0 \end{aligned}$$
(39)

from (10), (24) and (25). Since \(U^* > 0\) and \(r^\mathrm {eq} > \rho '\), \(\sin \theta ^\mathrm {eq} \ge 0\) and \(\cos \theta ^\mathrm {eq} \ge 0\) must hold, and then \(0 \le \theta ^\mathrm {eq} \le \pi / 2\). From (38) and (39), we obtain \(\sin \theta ^\mathrm {eq} - \sigma \cos \theta ^\mathrm {eq} = 0\), and \(\theta ^\mathrm {eq} = \arctan \sigma \).

Considering \(0 \le \theta ^\mathrm {eq} \le \pi / 2\),

$$\begin{aligned} \sin \theta ^\mathrm {eq} = \frac{\sigma }{\sqrt{1 + \sigma ^2}},~~ \cos \theta ^\mathrm {eq} = \frac{1}{\sqrt{1 + \sigma ^2}}. \end{aligned}$$
(40)

Substituting (40) into (38), \(r^\mathrm {eq} = \rho ' + \frac{U^*}{\sqrt{1 + \sigma ^2}a}\). Therefore, for \(U^* \in \left( 0, \sqrt{1+\sigma ^2}U'/2\right) \), the equilibrium \((r^\mathrm {eq}, \theta ^\mathrm {eq}) = \left( \rho ' + U^*/(\sqrt{1 + \sigma ^2}a) , \arctan \sigma \right) \) continuously exists in \(\rho '< r < r_c\), and \(r^\mathrm {eq}\) is monotonically increasing for \(U^*\).

(case 2: If \(r_c \le r^\mathrm {eq} \le r_e)\)  The equilibrium satisfies

$$\begin{aligned}&U^* \cos \theta ^\mathrm {eq} - a(r^\mathrm {eq} - \rho ') = 0,\end{aligned}$$
(41)
$$\begin{aligned}&\sigma \left( U' - a(r^\mathrm {eq} - \rho ') \right) - U^* \sin \theta ^\mathrm {eq} = 0 \end{aligned}$$
(42)

from (11), (24) and (25). Since \(U^* > 0\) and \(r^\mathrm {eq} > \rho '\), \(\sin \theta ^\mathrm {eq} \ge 0\) and \(\cos \theta ^\mathrm {eq} > 0\) must hold, and then \(0 \le \theta ^\mathrm {eq} < \pi / 2\). From (41) and (42), we obtain

$$\begin{aligned} \sin \theta ^\mathrm {eq} + \sigma \cos \theta ^\mathrm {eq} = \frac{\sigma U'}{U^*} \end{aligned}$$
(43)

and a condition of existence of \(\theta ^\mathrm {eq}\) is

$$\begin{aligned} U^* \ge \frac{\sigma U'}{\sqrt{1+\sigma ^2}}. \end{aligned}$$
(44)

Since \(\frac{\sigma U'}{\sqrt{1+\sigma ^2}} \le \frac{\sqrt{1+\sigma ^2}}{2}U'\) holds for any \(\sigma \in [0, 1]\) and \(U' \ge 0\), the equilibrium exists continuously in \(r_c \le r \le r_e\) for \(U^* \in \left[ \sqrt{1+\sigma ^2}U'/2, U' \right] \), which satisfies (44). Since \(\theta ^\mathrm {eq}\) (\(\ge 0\)) is monotonically decreasing for \(U^*\) from (43) and \(\theta ^\mathrm {eq} = \arctan \sigma \) at \(U^* = \sqrt{1+\sigma ^2}U'/2\), we have \(\theta ^\mathrm {eq} \le \arctan \sigma \) for \(U^* \in \left[ \sqrt{1+\sigma ^2}U'/2, U' \right] \). Moreover, \(r^\mathrm {eq}\) is monotonically increasing for \(U^*\) in the same way as in the first case.

At \(U^* = \sqrt{1+\sigma ^2}U'/2\), \((r^\mathrm {eq}, \theta ^\mathrm {eq})\) is continuous from equations (38), (39), (41) and (42). Thus, this lemma is proved.

Appendix C: Proof of Lemma 1 (stability of the equilibrium)

Consider fixed \(U^* \in (0, U']\) and the corresponding equilibrium \((r^\mathrm {eq}, \theta ^\mathrm {eq})\). Jacobi matrix J at \((r^\mathrm {eq}, \theta ^\mathrm {eq})\) is computed as follows:

$$\begin{aligned} J = \left[ \begin{array}{c c} - \, a &{}\quad - \, U^* \sin \theta ^\mathrm {eq} \\ J_{21} &{}\quad -\,\frac{U^*}{r^\mathrm {eq}}\cos \theta ^\mathrm {eq} \end{array} \right] , \end{aligned}$$

where

$$\begin{aligned} J_{21} = {\left\{ \begin{array}{ll} \frac{a\sigma }{r^\mathrm {eq}} ~~~~~ (\rho '< r^\mathrm {eq} < r_c), \\ -\frac{a\sigma }{r^\mathrm {eq}} ~~~ (r_c \le r^\mathrm {eq} \le r_e). \end{array}\right. } \end{aligned}$$

The characteristic equation of J is \(\lambda ^2 - (\mathrm {tr}J)\lambda + \mathrm {det}J = 0\), where \(\lambda \) is an eigenvalue of J. Since \(0 \le \theta ^\mathrm {eq} \le \arctan \sigma \le \pi / 4\) from Lemma 1, \(\mathrm {tr}J = -a -\frac{U^*}{r^\mathrm {eq}}\cos \theta ^\mathrm {eq} < 0\), and

$$\begin{aligned} \mathrm {det}J= & {} \frac{aU^*}{r^\mathrm {eq}}\cos \theta ^\mathrm {eq} \pm \frac{aU^*\sigma }{r^\mathrm {eq}}\sin \theta ^\mathrm {eq}\nonumber \\\ge & {} \frac{aU^*}{r^\mathrm {eq}}(\cos \theta ^\mathrm {eq} - \sigma \sin \theta ^\mathrm {eq}) \ge 0 \end{aligned}$$
(45)

are obtained. Here, the last equality of (45) is attained if and only if \(\sigma = 1\) and \(U^* = U' / \sqrt{2}\). Otherwise, we have \(\mathrm {Re}(\lambda ) < 0\) from the theorem of Hurwitz. Moreover, since \((\mathrm {tr}J)^2 - 4\mathrm {det}J >0\), \(\lambda \) is a real number, and \(\lambda \le 0\).

If \(\lambda < 0\), the equilibrium is stable. Otherwise, one of the eigenvalues equals 0. One of the eigenvectors corresponding to \(\lambda = 0\) is \([U^* / \sqrt{2}a, -1]^{\mathrm {T}}\). The equilibrium is \((r_0, \theta _0) = (r_c, \pi / 4)\), where \(u_{i\theta }\) is not differentiable. \(\lambda = 0\) is adopted to the positive direction of r, while \(\lambda < 0\) to the negative direction from (45). Thus, we consider the direction of the vector \([\varDelta r, \varDelta \theta ]^{\mathrm {T}} = \epsilon [U^* / \sqrt{2}a, -1]^{\mathrm {T}}\), where \(\epsilon > 0\). By the Taylor expansion of \(\dot{r}\) and \(\dot{\theta }\) around \((r^\mathrm {eq}, \theta ^\mathrm {eq})\), we have

$$\begin{aligned}&\dot{r}(r_0 + \varDelta r, \theta _0 + \varDelta \theta ) = -a\varDelta r - U^* \sin \theta _0 \varDelta \theta \nonumber \\&\qquad \qquad \qquad \qquad \qquad - \frac{U^*\cos \theta _0}{2}(\varDelta \theta )^2 + \dots ,\end{aligned}$$
(46)
$$\begin{aligned}&\dot{\theta }(r_0 + \varDelta r, \theta _0 + \varDelta \theta ) = -\frac{\sigma a}{r_0}\varDelta r\nonumber \\&\qquad \qquad - \frac{U^*\cos \theta _0}{r_0}\varDelta \theta + \frac{\sigma a}{r_0^2}(\varDelta r)^2 \nonumber \\&\qquad \qquad + \frac{U^*\sin \theta _0}{2r_0} (\varDelta \theta )^2 + \frac{U^*\cos \theta _0}{r_0^2}\varDelta r \varDelta \theta + \dots . \end{aligned}$$
(47)

Substituting \([\varDelta r, \varDelta \theta ]^{\mathrm {T}} = \epsilon [U^* / \sqrt{2}a, -1]^{\mathrm {T}}\) into (46) and (46), we have \(\dot{r}(r_0 + \varDelta r, \theta _0 + \varDelta \theta ) = -\frac{\epsilon a}{2\sqrt{2}}\varDelta r\), and \(\dot{\theta }(r_0 + \varDelta r, \theta _0 + \varDelta \theta ) = -\frac{U^*\epsilon }{2\sqrt{2}r_0}\varDelta \theta \), which show the equilibrium attracts points in the direction of \(\epsilon [U^* / \sqrt{2}a, -1]^{\mathrm {T}}\). Therefore, \((r^\mathrm {eq}, \theta ^\mathrm {eq})\) is stable.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yoshimoto, M., Endo, T., Maeda, R. et al. Decentralized navigation method for a robotic swarm with nonhomogeneous abilities. Auton Robot 42, 1583–1599 (2018). https://doi.org/10.1007/s10514-018-9774-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10514-018-9774-x

Keywords

Navigation