Sea Technology

MAR 2014

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44 st / March 2014 refection and maybe a potential obstacle. Converting the bright area into the obstacle area generates an obstacle map. The image processing comprises four steps, including fl- tering, enhancement, segmentation and binary processing. Filtering was used to suppress the noise of the original sonar image. The noise generally conducts itself with a minimum or maximum value and becomes higher with enhancement of the object's intensity. So, the fltering threshold needs to be dynamically determined by the image's average intensity. In the second step, the enhancement will further increase the signal-to-noise ratio of the fltered image and lay the ground work for the following image segmentation. In the segmentation of the third step, we present a fuzzy k-mean clustering algorithm, in which n data object is divided into k subclusters with internal clustering and external disper- sal. The clustering center updates according to the orienta- tion principle, while the mean square deviation serves as a similarity measure function. Finally, the binary processing translates the gray image into the binary image, in which obstacle grid is denoted by 1 and free grid denoted by 0. Strategic Decision Real-time obstacle avoidance decision making compris- es risk assessment and avoidance behavior selection. The risk assessment was used to judge the possibility of colli- sion according to the acquired obstacle information. It in- cludes two parts. The frst was to fnd the nearest obstacle, which may be alone, with angle or with azimuth. The sec- ond part was to apply fuzzy reasoning for risk assessment based on the nearest obstacles, and the fuzzy rules were determined by prior information of the AUV's maneuver- ability and fexibility. The avoidance behavior selection was of these cages were visible, and even the dam edges were wider and clearer than before. The other scenario occurred when no obstacles were ahead, and some false objects appeared in the sonar images because of environmental interference. For instance, a wake of a boat and a corps of stochastic air bubbles could be detected by the sonar. This scenario was more frequent and had a more serious infuence on real-time obstacle avoid- ance than the frst. These false objects may lead to mistaken behavior of avoidance and departure from the desired tra- jectory. This was not our desired state. Detection and Abstraction The real-time obstacle avoidance method based on im- aging sonar consists of two steps. First is sonar image pro- cessing, including obstacle detection and characteristic abstraction, which convert, through a series of image pro- cessing methods, an original image to an obstacle grid map that can be recognized by an AUV. The following step was a real-time decision of avoidance to determine the behav- ior for avoiding obstacles according to a real-time obstacle map, the defned mission requirement and the vehicle ki- nematics. The Blueview multibeam imaging sonar includes 768 beams in each ping. Its data update rate correlates highly with the detection range. The farther the range was set, the longer the interval between two pings. Typically the interval was about 600 milliseconds when the range was 100 me- ters. A software developer kit was provided by Blueview to accomplish the interaction between the control computer and the sonar. The computer acquires the gray image in real time, in which a bright point implies there was a stronger Two cages are clearly visible in the sonar image when the distance is about 50 meters. MarBook.indd 44 3/10/14 2:14 PM

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