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Applying Multibeam Imaging Sonar As an AUV's Obstacle Avoidance Sensor [Sea Technology](Sea Technology Via Acquire Media NewsEdge) Technique Enables More Accurate Information for Better Navigation Obstacle avoidance is an essential ability of autonomy for an AUV to work in a complex environment, which not only concerns the accomplishment of missions, but also influences safety. In addition, automatic detection, identification and avoidance of unknown obstacles are a sign of intelligence. There have been many research interests in obstacle avoidance in recent decades. Obstacle avoidance means that an AUV can autonomously sense unknown obstacles and adjust its trajectory to avoid collision in real time. The obstacle avoidance sensor plays a central role in detecting frontal objects and is fundamental to avoid collision. In practice, it seems to be the AUV's eye. The AUV depends on its output data to determine whether obstacles exist that would prevent its forward motion. Therefore, the information collected by the sensor in unit time has a direct effect on the AUV's obstacle avoidance architecture and method. Originally, there was widespread application for an echosounder acting as an obstacle avoidance sensor. But echosounders can only measure the distance of an obstacle in a defined-angle direction. To get the whole view, many echosounders would need to be installed on the AUV's nose. Recently, advances in sonar technology have enabled the development of reliable, high-resolution, multibeam imaging sonar that can acquire a real-time image of the perceived environment. More imaging sonar has been applied to AUVs to date. With the continuous increase in the application demands of an AUV in the rugged seafloor, there is a need for a sensing device like imaging sonar, which can acquire high-resolution images in real time and provide adequate information for avoiding obstacles. Challenge The sonar in our project is a P450-130S produced by Teledyne Blueview (Bothell, Washington), which is a 2D multibeam imaging sonar. An AUV integrated with the sonar underwent trials at Qiandao Lake in Zhejiang, China in August 2013. Each ping image from this sonar shows the objects seen at the 130° horizontal angle of view and the 15° vertical angle of view. Some special characteristics in this imaging sonar were determined by its working theory. Firstly, there may be a shaded part behind an object when the sonar sees distant or large objects, similar to a lamp casting light on some objects. Its view distance became very small when it was close to the seafloor or an obstacle. Finally, it cannot determine the object's position in a vertical direction because it was only a 2D sonar without vertical resolution. In practice, there were two scenarios in terms of realtime avoidance decision making. One was whether the sonar could accurately "see" the objects that appear in front of the AUV, which directly determines whether the AUV can avoid them. For example, two cages were hung down the surface beside an underwater right-angle dam. In the first scenario, we could not see the two cages from the sonar images when the distance between them was larger than 150 meters. When the distance was nearer and almost 100 meters, the blur of these cages loomed but was not clear. Until the distance was about 50 meters, the foursquare outlines 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 influence on real-time obstacle avoidance than the first. These false objects may lead to mistaken behavior of avoidance and departure from the desired trajectory. This was not our desired state. Detection and Abstraction The real-time obstacle avoidance method based on imaging sonar consists of two steps. First is sonar image processing, including obstacle detection and characteristic abstraction, which convert, through a series of image processing 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 behavior for avoiding obstacles according to a real-time obstacle map, the defined mission requirement and the vehicle kinematics. 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 meters. 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 reflection and maybe a potential obstacle. Converting the bright area into the obstacle area generates an obstacle map. The image processing comprises four steps, including filtering, 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 filtering 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 filtered 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 dispersal. The clustering center updates according to the orientation 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 comprises risk assessment and avoidance behavior selection. The risk assessment was used to judge the possibility of collision according to the acquired obstacle information. It includes two parts. The first was to find the nearest obstacle, which may be alone, with angle or with azimuth. The second 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 maneuverability and flexibility. The avoidance behavior selection was defined as a procedure to determine a real-time reactive behavior for avoiding the confirmed obstacles according to the processed sonar image. Its input was the risk degree of the nearest obstacle, and its output was horizontal or vertical avoidance behavior defined by the variable quantities of heading, depth or height, and velocity. The sonar image region was divided into three subregions based on the Blueview multibeam sonar's characteristics, including a danger region (-21°, 21°), a gaze region (-43°, -21° and 21°, 43°), and an observation region (-65°, -43° and 43°, 65°). Assuming that the detection range was set to 100 meters, there were four ranks with a 25-meter distance interval from near and far. Obviously, the obstacle that was in the danger region at a shorter distance from the AUV was more dangerous and needed to be avoided instantly. But the obstacle in the gaze region requires close attention to the change in relative distance to the AUV. If it remains unchanged or gradually becomes larger, it can be concluded that the AUV is passing the obstacle. When the AUV has passed the obstacle, it will move into the observation region and eventually disappear from the sonar's view with the vehicle's motion. The decision's output to the AUV's motion controller was a series of avoidance behaviors including emergency steering, emergency ascend, keeping distance and keeping height. The emergency steering behavior was designed to bypass the obstacles in the horizontal plane by the way of controlling the AUV's deviation from the original heading. The emergency ascend behavior may keep the AUV up and increase its height continuously in order to achieve the purpose of vertical climbing. The distance maintenance behavior aims at maintaining a certain distance between the AUV and the obstacle's edge through keeping a constant distance to the left or right of the obstacle. This behavior makes sure that the AUV can completely avoid the obstacle without switching between steering and sailing in a straight line. The height maintenance behavior, same as the distance maintenance behavior that keeps the AUV parallel to the seabed contour, can convert to the emergency ascend behavior and target-heading behavior. The avoidance behavior selection was very comprehensive because it involved not only risk of collision, but also the requirements in the AUV's trajectory and mission, the limits of the AUV's maneuverability, and the user's specified requirements. Based on these special requirements, different methods were designed in the decision module. For example, if tracking a path was required, the vertical avoidance behaviors would be preferred. The methods were executed according to the requirements' priorities and the next desired actions, including action identity, the desired heading, the desired velocity and the desired depth/ weight. Conclusion Sonar technology will update constantly in the future. On the one hand, multibeam imaging sonar will be improved though better performance; smaller size, weight and power consumption; and increased AUV suitability. On the other hand, AUVs will be demanded in more complicated operation environments and will need a sensor, such as imaging sonar, that can gather more environmental information for intelligent decision making. Imaging sonar can improve an AUV's autonomy and will be applied more to AUV products as an optional sensor. But there are still some problems to resolve for real-time obstacle avoidance based on imaging sonar. The first is how to eliminate fake obstacles like wake and bubbles and to reduce improper avoidance reaction when an AUV sails near the surface. It is also very difficult to detect and identify vessels, boats and buoys near the surface, which is a potential risk for an ascending AUV. Finally, it is also complicated to differentiate obstacles from a rough seafloor with lots of reefs. More research on sonar processing methods is desirable to solve these problems, including the improvement of sonar and advancement of identification intelligence. Acknowledgments The authors would like to thank the staff at the Autonomous Underwater Vehicles Technology Department in SIA for helping with the lake trial. This project was supported by the Chinese National 863 Plan Program under the grant 2011AA09A102. Hongli Xu received a Ph.D. in pattern recognition and intelligent systems from the University of Chinese Academy of Sciences in 2009, and then began her career at the Shenyang Institute of Automation CAS. Her research areas focus on autonomous control, path planning and cooperative control of AUVs. Lei Gao received a master's degree in engineering from Harbin Engineering University in 2007. He joined the Shenyang Institute of Automation CAS in 2012 and works as a research and development engineer focusing on sonar image processing. (c) 2014 Compass Publications, Inc. |