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Abstract

Multiple autonomous mobile robot system has attracted increasing attention from scholars for its spatial and functional distributivity, high fault tolerance, strong robustness and many other advantages. Aiming at numerical instability, huge calculation amount, poor precision and other problems existing in the synergetic dynamic object tracking of multiple mobile robot in unknown complex environment, this paper proposes the covariance intersection multirobot object tracking algorithm based on self-adaption SR-CKF. The algorithm is distributed and it can improve the accuracy of the evaluation on relevant objects without independence assumption for data information, and thus avoids the evaluation of the cross correlation among objects’ status. In addition, targeting at bad observation information, the self-adaption SR-CKF is built on the basis of the information covariance matching principle, which has improved the robustness of the whole system. The simulation result has proved that this algorithm can effectively solve the problems in multirobot synergetic objects tracking in unknown environment.

Keywords

Multirobot covariance set object tracking synergetic

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References

Wang

C.C.

Thorpe

and Thrun

, Online simultaneous localization and mapping with detection and tracking of moving objects: Theory and results from a ground vehicle in crowded urban areas, IEEE International Conference on Robotics and Automation, 2003, Proceedings, ICRA IEEE, (2003), 842–849.

Ming

W.U.

Linlin

L.I.

and Sun

, PDA-IMM based moving object tracking with mobile robots in unknown environments, Robot 34(6) (2012), 668.

Liu

et al., Mobile robot moving object tracking based on modified Camshift algorithm, Journal of Huazhong University of Science and Technology (2011).

K.K.

and Ahn

H.S.

, Formation control and network localization via orientation alignment, IEEE Transactions on Automatic Controlk 59(2) (2014), 540–545.

Hollinger

G.A.

et al., Distributed data fusion for multirobot search, IEEE Transactions on Robotics 31(1) (2015), 55–66.

Luo

Chakraborty

and Sycara

, Provably-good distributed algorithm for constrained multi-robot task assignment for grouped tasks, IEEE Transactions on Robotics 31(1) (2015), 19–30.

Peng

et al., Leader–follower formation control of nonholonomic mobile robots based on a bioinspired neurodynamic based approach, Robotics and Autonomous Systems 61(9) (2013), 988–996.

Mariottini

G.L.

et al., Vision-Based Localization for Leader–Follower Formation Control, IEEE Transactions on Robotics 25(6) (2009), 1431–1438.

Cui

et al., Leader–follower formation control of underactuated autonomous underwater vehicles, Ocean Engineering 37(17) (2010), 1491–1502.

10.

Boutalis

Y.S.

and Kosmidou

O.I.

, A feedback linearization technique by using neural networks: Application to bioprocess control, IOS Press, 2006.

11.

Wang

Zhang

and Shi

, Research on aircraft engine actuator and sensor faults diagnosis with Takagi-Sugeno fuzzy model and H^∞ observer, Journal of Computational Methods in Sciences and Engineering 15(3) (2015), 423–436.

12.

Wang

and Wang

Z.B.

, A technological research of high temperature float glass thickness un-touch detection based on CCD sensor, Journal of Computational Methods in Sciences and Engineering 15(2) (2015), 251–260.

13.

Yamaguchi

, A cooperative hunting behavior by mobile-robot troops, IEEE International Conference on Robotics and Automation, 1998, Proceedings IEEE Xplore (1999), 3204–3209.

14.

Yamaguchi

, A distributed motion coordination strategy for multiple nonholonomic mobile robots in cooperative hunting operations, Robotics and Autonomous Systems 43(4) (2003), 257–282.

15.

Brinon

Seuret

and Canudasd

W.C.

, Translation control of a fleet circular formation of AUVs under finite communication range, Decision and Control, 2009 Held Jointly with the 2009, Chinese Control Conference, Cdc/ccc 2009. Proceedings of the, IEEE Conference on IEEE, (2009), 8345–8350.

16.

Arranz

L.B.

et al., Collaborative estimation of gradient direction by a formation of AUVs, Decision and Control and European Control Conference IEEE (2012), 5583–5588.

17.

Sun

Y.G.

Wang

and Xie

, Average consensus in networks of dynamic agents with switching topologies and multiple time-varying delays, Systems and Control Letters 57(2) (2008), 175–183.

18.

Ceccarelli

et al., Collective circular motion of multi-vehicle systems, Decision and Control, 2005 European Control Conference, Cdc-Ecc '05. IEEE Conference on IEEE (2008), 740–745.

19.

Wang

et al., Multi-sensor covariance intersection fusion attitude estimation with unknown noise characteristics, Control and Decision (2016).

20.

et al., Relative position cooperative localization algorithm of multi-robot based on SR-CKF, Journal of Electronic Measurement and Instrumentation (2016).

21.

Liu

Zhang

and Wu

, Adaptive target tracking based on distributed dynamic cluster in WSN, Chinese Journal of Sensors and Actuators (2012).

Covariance intersection multirobot object tracking algorithm based on self-adaption SR-CKF

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