AUV state2of2the2art and prospect

Transcription

1 1 1 Vol CAA I Transactions on Intelligent Systems Mar. 2006,,, (,150001) :,.,.,,. :.,.,. :; ; ; : TP24 :A : (2006) AUV state2of2the2art and prospect XU Yu2ru, PAN G Yong2jie, GAN Yong, SUN Yu2shan ( State Key Laboratory of Underwater Vehicle Technology, Harbin ,China) Abstract :Autonomous underwater vehicle(auv), as a sophisticated system integration of artificial intelligence, under2 water target detection and identification, data fusion, intelligent control, communication and navigation sub2systems, is an unmanned platform to carry out various civil and military missions in complex ocean environments. Owing to the expecta2 tion that AUV will be very promising in maritime research and ocean development and it also may find its way of applica2 tion in the future underwater conflict for intelligence acquisition, accurate attack andasymmetric intelligent acupunctural warfare, AUV tech is an important and active field of R &D,especially in the big powers of the world. State2of2the2art and main trend of development are presented based on our own (domestic) practice and experiences in related technical problems and suggestive proposals for probable way of solution to the problems are offered. The related areas are as fol2 lows: vehicle (carrier/ platform) design, architecture, control, intelligent planning and decision making, AUV simulation and virtual reality, underwater target detection and identification, reliability and fault2tolerant techniques,etc. With the development of science and artificial intelligence, we ll see encouraging results in many enabling techniques for AUV. However, AUV technology is still in its stage of basic research, test and development, and there is still a long way to go for its full availability. Keywords :auto no mo us underwater vehicle ; artificial intelligence ; system architect ure ; intelligent co nt rol ( ) 2,,., : : ( ). 21.,.,

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3 1,: 11, 2,,0195 m,80 kg, 50 m, 210 kn,, 15 km,. 2 2 Fig12 Mini underwater vehicleweilon G2 112,.,,,, ( )...,, ( ),.,,. ( NASA) ( NBS) NASREM [3 ].,,. ( MIT) ( Rodney A. Brooks),, [4 ].., 2.,.,,.,..., ( 3 ),,6 3 ( 4 ). 6 ;3 ; 20 ( ). 2005, [5 ]. 3 Fig1 3 Combining manner between denotation consequence and ANN 113.,.,, ( ),.,. [6 ].,.,,,.

4 12 1,,,,,. 4 Fig14 Software architecture of AUV PID 3,, 3,,, PID,,, S [7-8 ]..,. 114.,.,.,2., Z, X. X, ; [9 ].,,,,,, 5.,,. Q2, Q,.,., 6 7. Q2

5 1,: 13 5 Fig1 5 Path planning result at sea trial. 6 Fig16 Track of AUV during the beginning of learning period 7 Fig1 7 Track of AUV after learning for a period of time 115,,.,,.,,,.,,.,, [10-11 ]. SGI,,. SGI.,.,,,.,,,, [13 ].. SGI Per2 former.,,. SGI,,..,., ( ) [14 ]..,,..,. 8., (),,,

6 Fig18 Obstacle avoidance simulation result. 116,,,.,.,,.,,,. TV,,.,,, TV,TV,.,,,, ,.,.... :, ;, ;,,,,,.,,.,.,,,.,..,.,,.,,.,,..,,,, 9. 9 SeaBat Fig19 Object recognition with SeaBat

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