1. URV Lab, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016; 2. Shenyang University of Technology, Shenyang 110023, China
Abstract:The ocean environment is always uncertain, unstructured and unknown. Furthermore, the obstacle avoidance sonars of long distance AUV (LAUV) only have restricted abilities to get environmental information. These facts make it very difficult to build a precise, complete and general 3 dimension model for ocean environment. Real time Obstacle Avoidance (ROA) of LAUV is a dynamic process with distinct real time characteristics. It is related to ocean environment, as well as kinematics restriction, dynamics and maneuver. To solve these problems, a fuzzy obstacle avoidance algorithm based on complex control strategy is presented in this paper.
[1] Oussama Khatib. Real-time obstacle avoidance for manipulator and mobile robots [J].International Journal of Robotics Research,1986, 5(1): 90-98. [2] Johann Borenstein, Yoram Koren. Real-time avoidance for fast mobile robots [J].IEEE Transactions on Systems, Man and Cybernetics, 1989, 19(5): 1179-1187. [3] Johann Borenstein, Yoram Koren. The vector field Histogram-fast obstacle avoidance for mobile robots [J].IEEE Transactions on Robotics and Automation, 1991,7(3): 278-288. [4] Iwan Ulrich, Johann Borenstein. VFH*: Reliable obstacle a voidance for fast mobile robots [A].Proceeding of the IEEE International conference on Robotics &Automation [C], 1998:1572-1577. [5] Iwan Ulrich, Johann Borenstein. VFH*: Local obstacle avoidance with look-ahead verification [A].Proceeding of the IEEE International conference on Robotics &Automation [C], 2000: 2505-2511. [6] Ota J, Arai T, Yoshida E, Kurabayashi D, Sasaki J. Motion skills in multiple mobile robot system [J].Robotics and Autonomous System.1996, 8(19): 57-65. [7] Xuemin Liu, Hang Peng, Jiawei Li, Yru Xu. Obstacle avoidance using fuzzy neural networks [A].Proceedings IEEE Oceans [C],1998: 282-286. [8] 张汝波,顾国昌,张国印.水下智能机器人模糊局部规划器设计[J].机器人,1996,18(3):158-162. [9] Chohra A, Farah A, Belloucif M. Neuro-fuzzy expert system E S CO V for the obstacle avoidance behavior of intelligent autonomous vehicles[J].Advanced Robotics, 1999, 12(6): 629-649.