ROBOT AUV. A Method of Instruction Understanding for AUV Control Layer Based on Attention Mechanism

Transcription

1 ROBOT Vol.34, No.4 July, 01 DOI /SP.J AUV 1, 1, 1, 1, AUV Itti AUV TP4 A (01) A Method of Instruction Understanding for AUV Control Layer Based on Attention Mechanism LI Yueming 1, WAN Lei 1, SUN Yushan 1, ZHANG Guocheng 1, (1. Key Laboratory of Science and Technology on Autonomous Underwater Vehicle, Harbin Engineering University, Harbin , China;. College of Shipbuilding Engineering, Harbin Engineering University, Harbin , China) Abstract: In order to maintain an appropriate relationship between the planning layer and the control layer of autonomous underwater vehicle (AUV), with Itti visual attention model considered as a reference, a planning instruction understanding (PIU) model based on attention mechanism is presented, and the processes of PIU and the secondary planning are established. The attention focus of planning instruction is obtained using fuzzy reasoning for analyzing the feature and significance of instruction element. Furthermore, the secondary planning is implemented adopting attention focus, which can ensure a higher priority execution of the state concerned by the planning layer. The simulation results demonstrate that the proposed PIU process can not only realize the acquisition of the instruction intention of planning layer, and the planning instruction can be executed more effectively, but also the motion control effect is heightened and the intelligence level of control layer is improved. Keywords: autonomous underwater vehicle (AUV); instruction understanding; attention model; planning 1 Introduction [1] 6 [] AUV [3] [4] [5] AA each-day@163.com / /

2 34 4 AUV 407 Planning instruction understanding and secondary planning in the control layer XX AUV [6] [1] 1 Fig.1 1 XX AUV Architecture of the XX AUV system 6 3 AUV Planning instruction understanding for AUV Itti Itti [7-8] 3 Fig. Planning instruction understanding model 3.1 E ĖE s ψ z u s ψ z ū E = [e 1 e e 3 e 4 ] T = [ s s ψ ψ z z ū u] ĖE = [ė 1 ė ė 3 ė 4 ] T (1) {NB,NS,ZO,PS,PB} 3 E ĖE 3. O = {UK,FO, ST,PS,PB}

3 NB NS ZO PS PB 0.5 Tab. Rules list for attention focus Fig Membership function of instruction element feature UK FO ST PS PB 0.5 a UK FO ST PS PB UK UK UK UK AD AI FO UK UK UK AD AI o 1 ST UK UK UK AD AI PS FD FD FD FD FD PB FI FI FI FI FI o Fig Membership function of instruction element saliency IF-THEN IF e is e i and ė is e j THEN o is o k 5 1 Tab.1 1 Rules list for desired instruction element saliency o NB NS ZO PS PB NB PB PB PB PS PS NS PB PS ST FO UK e ZO UK FO ST FO UK 3.3 PS UK FO ST PS PB PB PS PS PB PB PB 4 A = {FI,FD,UK,AD,AI} 3 IF-THEN IF o 1 is o 1i and o is o j THEN a is a k 5 A 4 Secondary planning ė e 1 e e e 1 a 1, e s1 e s {es1 = (1 S(a 1,))e 1 + S(a 1,)e 1 e s = (1 S(a 1, ))e + S(a 1, )e () S(a) = a/( a + δ) [9] [ 1,1] δ 4. e

4 34 4 AUV 409 e 3 ϕ min u Tab.3 3 Rules list of desired instruction element s ϕ z u s s ϕ min z ϕ s ϕ u z s z u ū ϕ z ū z ϕ s u 5 Simulation XX [10] 5 5 Fig.5 Simulation flow chart 0 (0m,0m) 0 m (10m,10m) 3 m 90 North /m North /m North /m depth /m East /m (a) East /m (b) East /m (c) time /(0.5 s/step) Fig.6 (d) 6 Results of simulation 1

5 (a) (c) 3 5 s 6(d) Dep Att Yaw Att attention attention Fig Yaw_Att Dep_Att time /(0.5 s/step) Fig.8 7 Attention curves in the second simulation time /(0.5 s/step) Yaw_Att Dep_Att Attention curves in the third simulation (1) 6(a) () 6(b) (3) 6(c) (4) 6(d) 3 1 (5) Conclusion Itti References [1] [J] 006 1(1): Xu Y R, Pang Y J, Gan Y, et al. AUV state-of-the-art and prospect[j]. CAAI Transactions on Intelligent Systems, 006, 1(1): [] S [J] 01 46(): Li Y M, Pang Y J, Wan L. Adaptive S plane control for autonomous underwater vehicle[j]. Journal of Shanghai Jiaotong University, 01, 46(): [3] [D] 007 Gan Y. Research on motion control system architecture of underwater vehicle[d]. Harbin: Harbin Engineering University,

6 References [1] Alvarado P V Y. Design of machines with compliant bodies for biomimetic locomotion in liquid environments[d]. Massachusetts, MA, USA: Department of Mechanical Engineering of MIT, 006. [] Alvarado P V Y, Youcef-Toumi K. Design of machines with compliant bodies for biomimetic locomotion in liquid environments[j]. Journal of Dynamic Systems, Measurement and Control, 006, 18(1): [3] Epps B P, Alvarado P V Y, Youcef-Toumi K, et al. Swimming performance of a biomimetic compliant fish-like robot[j]. Experiments in Fluids, 009, 47(6): [4] [D] 008 Yan Y. Flow physics of freely routine swimming of koi carp[d]. Hefei: University of Science and Technology of China, 008. [5] Wu G H, Yang Y, Zeng L J. Kinematics, hydrodynamics and energetic advantages of burst-and-coast swimming of koi carps (Cyprinus carpio koi)[j]. Journal of Experimental Biology, 007, 10(1): [6] [D] 010 Xia D. Study on mechanism of thunniform bio-inspired mode swimming under self propulsion[d]. Harbin: Harbin Institute of Technology, 010 [7] Wen L, Wang T M, Liang J H, et al. A novel method based on a force-feedback technique for the hydrodynamic investigation of kinematics effects on robotic fish[c]//ieee International Conference on Robotics and Automation. Piscataway, NJ, USA: IEEE, 011: [8] [P] Wang X S, Xu J L. Bio-mimetic robotic fish: China, [P], [9] Ang S J. Numerical simulation of moving problems with application to fish swimming[d]. Singapore: National University of Singapore, 007. [10] Wang X Y, Yeo K S, Chew C S, et al. A SVD-GFD scheme for computing 3D incompressible viscous fluid flows[j]. Computers & Fluids, 008, 37(6): [4] Naeem W, Sutton R, Ahmad S M, et al. A review of guidance laws applicable to unmanned underwater vehicles[j]. Journal of Navigation, 003, 56(1): [5] [J] (suppl.): Liu X M, Pang Y J, Xu Y R. A fuzzy trajectory tracking method based on virtual sensor information[j]. Robot, 1998, 0(suppl.): [6] [J] 004 6(3): Gan Y, Wang L R, Liu J C, et al. The embedded basic motion control system of autonomous underwater vehicle[j]. Robot, 004, 6(3): [7] Itti L, Koch C. Computational modeling of visual attention[j]. Nature Reviews Neuroscience, 001, (3): [8] [J] 010 0(11): Zhang J, Wei W. Saliency extraction based on visual attention model[j]. Computer Technology and Development, 010, 0(11): [9] [C]// 7 008: Li Y M, Guo B J, Pang Y J. A method for weakening the chattering of sliding mode controller[c]//proceedings of the 7th Chinese Control Conference. Beijing: Beihang University Press, 008: [10] [J]00(3): Chang W J, Liu J C, Yu H N, et al. A mathematical model for motion control and simulation of underwater vehicle[j]. Ship Engineering, 00(3):