HANSE - A Low-Cost Autonomous Underwater Vehicle

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1 HANSE - A Low-Cost Autonomous Underwater Vehicle Dariush Forouher, Jan Hartmann, Jan Helge Klüssendorff, Erik Maehle, Benjamin Meyer, Christoph Osterloh, and Thomas Tosik Institute of Computer Engineering, University of Lübeck, Ratzeburger Allee 160, Lübeck, Germany Abstract. HANSE is a low-cost Autonomous Underwater Vehicle (AUV) capable of solving many common underwater challenges. In this paper we will present HANSE s modular and expandable hardware and software design, the underwater simulator MARS, as well as robust and efficient sonar-based localization and vision-based object detection algorithms, with which we have successfully participated in the Student Autonomous Underwater Vehicle Challenge in Europe (SAUC-E) Introduction Autonomous underwater vehicles (AUV) have become increasingly popular, for example in the fields of environmental monitoring or inspection of underwater structures. Underwater environment is a challenge to autonomous robots, as it limits both sensing and actuation. Changes in the environment, e.g. water currents, direct sunlight, or floating obstacles, may affect the robot significantly. HANSE is a low cost AUV with an innovative commodity hardware-based, modular, and expandable design. It is able to solve localization using sonar sensors and an attitude and heading reference system (AHRS) only and provides robust vision-based underwater object detection and tracking. We will present the HANSE AUV, its design, and its algorithms in this paper. Robotic competitions, e.g. the RoboCup, have shown to improve research and promote international collaborations. The Student Autonomous Underwater Vehicle Challenge in Europe (SAUC-E) 1 provides a possibility to demonstrate solutions to a variety of problems of underwater robotics, including localization and navigation, visual and acoustic object detection and tracking, or manipulation. We have participated in SAUC- E in the last three years with HANSE. Our experiences at the competition will be used to evaluate our design choices and algorithms. 1.1 Related Work Ten teams from nine different European universities have competed in SAUC-E AUVs are typically cylindrically shaped, one major difference to HANSE. Most AUVs have scanning sonars and AHRS for localization, and multiple cameras for object detection. Teams with large budgets, for example Hariot Watt University with Nessie 1

2 Fig. 1. The autonomous underwater robot HANSE. IV [MCJ + 09], winner of SAUC-E 2009, and the University of Girona with Sparus, winner of SAUC-E 2010 [APL + 11], can afford additional and higher quality sensors, e.g. Doppler Velocity Logs (DVL) for odometry and additional sonar sensors. Especially the inclusion of a DVL may be an advantage. Ribas et al. describe an approach to Simultaneous Localization and Mapping (SLAM) in [RRTN07] using a scanning sonar. They extract line features from sonar images using the Hough transform and use them as landmarks for localization. Distortions due to movement of the vehicle are corrected by estimating the movement of the robot with the help of a DVL and correcting the sonar images accordingly, resulting in an accurate and robust localization. A comparison of different camera-based object tracking algorithms in underwater environments has been discussed in [SD06]. A feature-based approach for visual SLAM has been shown in [ACL + 10]. For HANSE, we use a much simpler color-based object detection algorithm, which nevertheless proved to be accurate and robust. Generally, there are two main differences between HANSE and other similarly successful AUVs at the SAUC-E competition. Firstly, our AUV HANSE was built on a comparatively low budget, limiting the choices in both mechanical design and sensors. Secondly, HANSE was built and maintained mainly by undergraduate students, which again significantly affected the hardware and software design. This paper will therefore focus on these two aspects. 2 Design Our design decisions are based on the SAUC-E mission rules on the one hand and budget limits and student requirements on the other. The SAUC-E mission rules state the general tasks or missions the AUV must perform as well as competition rules. Missions include, for example, following a yellow pipeline on the seabed, following a harbour wall, or tracking a surface vehicle. We generally see localization and object detection as the main challenges at SAUC-E. Localization is needed to navigate between missions and provide feedback on success or failure of a mission. Object detection is needed to identify the mission target. The main focus should be on robustness, as several runs have to be successfully performed during different times of day and potentially different weather conditions. Due to the limited budget, we have built HANSE with part costs of less than 14,000 Euro. We therefore could only include the most necessary sensors, e.g. a scanning sonar for localization and multiple cameras for object detection.

3 Fig. 2. Electronical components of HANSE The student participation affects the development process and the choice of algorithms. The amount of work that can be put into the development of the AUV through practical courses and theses is limited, the AUV must therefore be iteratively designed over several years, requiring a modular and expandable design. We further heavily rely on our underwater simulator MARS, which enables students to develop and thoroughly test algorithms without the need of the actual robot. 2.1 Hardware The biggest challenge in this competition, by far, is the construction of the AUV. HANSE is by its nature an experimental prototype, requiring high maintainance and multiple members of the team to use. Acknowledging that, we kept HANSE s design simple, so that failures and breakdowns can be repaired easily, often within minutes. All parts are easily accessible and easy to replace. The base frame of our AUVs is shaped like a sledge. All sensors and thrusters are attached to the frame using pipe clamps, allowing for easy reconfiguration. The base frame of the AUV is made out of 50 mm Polypropylene (PP) tubes. We have chosen this material because of its light weight and the possibility of welding single parts together easily. To increase the solidity of the frame, it was strengthened by glass fiber sheathing. Drilled holes allow the flooding of the frame. It therefore has near neutral buoyancy. A commercial waterproof case, designed for photo equipment, is fixed on the base frame and serves as the main pressure hull of the robot. With inside measurements of 30x22.5x13.2 cm, the case contains the laptop, batteries, and auxilary electronics. Similar designs have also been proposed for hobby AUVs (see e.g. [Boh97]). Thrusters, sonar devices, pressure sensor and cameras are placed outside the main case and connected to the case by waterproof connectors (BULGIN Buccaneer). To ensure stable horizontal orientation of the robot, additional lead weights are attached to the frame. Fig. 2 gives an overview of the major electronic components of HANSE. The central processing unit of HANSE is a 12.1 laptop. All peripheral hardware is connected by USB. Motor controller and pressure sensor are accessed via an I2C bus. An Atmegabased microcontroller board (Chip45 Crumb168) acts as a bridge between the laptop and the I2C bus.

4 Fig. 3. Snapshot of the simulation environment MARS. 2.2 Software We chose to develop a custom software framework for HANSE, the main reason being that no cross platform framework was available and we did not want to commit to an operating system at that time. The development of a custom, light-weight framework further eases the introduction to new students joining the team and increases the degree of control over the system. The framework consists of a slim core, which includes the raw graphical user interface and helper classes, i.e. logging, serial port interface and a configuration storage. It is further divided into modules, each module executed in its own thread. Modelled after the data flow of the robot, modules are arranged in a directed acyclic graph. Modules can be roughly separated into three levels: drivers, behaviours, and tasks. Driver modules provide an abstracted access to low-level hardware, e.g. thrusters and sensors. High-level algorithm modules process the sensor data and provide information for the behaviour modules. A task module encapsulates different behaviours of a SAUC- E mission. Both behaviours and task are implemented using state machines. 2.3 Simulation AUVs are operating in a risky and ever-changing environment. Due to the complexity of such a system as an AUV extensive testing is necessary to minimize these risks. One solution to this problem is the use of simulations. There are several underwater simulators, e.g Neptune [RBRC04], MarineSim [SWL + 10], or SubSim [BB06]. But these are either not available to us (Neptune), provide only rudimentary underwater simulation (MarineSim and Subsim), or are closed source and not extendable (Subsim). We have therefore developed our own simulator: the Marine Robotics Simulator (MARS, see Fig. 3). MARS is a hardware-in-the-loop (HIL) simulation environment for multiple AUVs written mainly in Java. It uses the Bullet Physics Library 2 as a physics engine and 2 Bullet Physics Library,

5 Fig. 4. Illustration of the sonar image filter chain. Left: A 360 scan as provided by the scanning sonar (in polar coordinates). Center: Sonar image after applying the gradient filter. Right: extracted wall features after applying non-maximum suppression and heuristics. jmonkeyengine3 3 for 3d-rendering. MARS runs independently of the HANSE framework and offers a straight-forward TCP connection. To ensure a natural behaviour of the AUV, physics, world model and sensor/actuator models have to be as realistic as possible. For the physics part static buoyancy, drag, waves and currents has been taken into account. The volume and center of buoyancy of the AUV are calculated through ray collisions depending on the current water height. Drag is simulated by parallel projection and pixel counting. The world model consists of a height map-based terrain, in which other objects like buoyages or pipelines can be placed. To perceive and interact with the world several sensors and actuators were implemented. The sonar is simulated through raycasting, where several rays are colliding with the world. The length and the angle of impact are taken into account, resulting in a more realistic sonar image. The sonar noise is based on experimental data. The simulated cameras allow more natural-looking underwater environments through the use of various filters, e.g. underwater-fog and depth-of-field. The thruster model is based on experimental data taken from the Seabotix thrusters. To all sensors and actuators noise or an own noise function can be added. With the use of MARS, we were able to perform thorough dry-dock testing of all behaviours and higher level code in HANSE. Complete runs with several tasks could be performed and several team members could work in parallel and evaluate our algorithms, resulting in a faster development cycle. 3 Localization In underwater scenarios, localization is a challenging problem [RRTN07]. A scanning sonar provides information about the environment and an AHRS provides information about the robot s orientation. Without the use of a DVL for odometry one challenge lies in the slow update rate of the scanning sonar, which takes at least 8 sec. for a 360 turn. Another challenge is the robust extraction of features from the noisy sonar images. Our localization algorithm is based on a landmark-based particle filter framework as described in [FHLM11]. We have further improved feature extraction using a multiscale gradient filter to enhance the regions of the sonar image characteristic to walls, 3 jmonkeyengine 3,

6 which are used as landmarks. 1D Haar wavelet responses at different scales are multiplied for each beam pixel to form the beam gradient G as ( x G(x) = B(i) k K i=x k x+k i=x+1 B(i) ), (1) where x is the distance from the robot, K is the set of all scales to be evaluated, and B(i) is the echo intensity at distance i. The Haar wavelet responses can be efficiently calculated using integral images. The filtered sonar image is then analyzed to find wall locations. First, a non-maximum suppression is performed to identify potential walls. For each beam, all local maxima that have a gradient intensity of less than one tenth of the maximum gradient intensity in the last sonar image are discarded. Further heuristics concerning the neighbors of wall features are applied, based on assumptions on continuity of walls and minimum lengths of wall segments. The result of the filter chain is illustrated in Fig Object Detection The challenge for the computer vision algorithm lies in the robustness to changing outer conditions, e.g. different lighting or particles in the water, as well as the bad image quality of the low cost webcams that were used. A visual object detection is thus performed based on color-based segmentation rather than edge or corner-based feature extraction techniques. Each image is segmented in the appropriate color channel (e.g. red for the pipeline) using Otsu s algorithm for automatic threshold selection [Ots79]. This method provides an efficient object detection, being fairly robust to image noise and lighting. The presence of an object is decided based on the number of pixels belonging to the object class and the mean position of these pixels. The object orientation θ can further be found using centralized image moments [BSA91] of the segmented image. An example of the object detection algorithm can be found in Fig. 5. The extracted information can now be used by simple homing and following behaviours to perform a mission, e.g. follow the yellow pipeline. Fig. 5. Object detection using segmentation. Left: RGB image. Center: segmentation channel (red). Right: Otsu thresholding. The blue line indicates the position and direction of a yellow pipeline.

7 y in m x in m Fig. 6. Robot path estimated by the localization algorithm at the SAUC-E competition. The start point is at the red circle, the end point is at the red square. Walls are depicted in thick black. Different phases of the run are shown as described in the text. 5 Evaluation An evaluation of the presented hardware and software design and algorithm performance at SAUC-E is difficult due to the lack of ground truth data. The performance of the presented localization and object detection algorithms may be inferred from data recorded at the competition. Fig. 6, for example, shows the estimated robot path for a qualifying run at the SAUC-E The AUV started at the side of the test site and moved to the pipeline in the center of the test site (dotted black line). The target point is accurately reached, as the pipeline has been successfully found. The curved pipeline has been followed back and forth (solid red line). The path in this section closely resembles the actual pipeline, indication of both accurate localization and object detection. The AUV was then moved to the lower right to start the wall following mission (dotted black line). The wall was now being followed at a distance of 5 m till the end of the run (solid blue line), which matches the distance from the wall estimated by the localization algorithm. Generally, the successful completion of several missions was performed repeatedly in qualifying, semi-final, and final runs, an indication for the robustness of our algorithms. Pipeline following and wall following were successfully completed in all runs, which proofs the robust approach to underwater object detection and the stable localization, which was used to navigate from the start to the pipeline as well as from the pipeline to the wall to be followed. We have further been the only team to successfully find and cut loose the red buoy. 6 Conclusion In this paper, we have presented our AUV HANSE. A number of design decisions make HANSE special. With a limited budget and the focus on student participation in the development of both hard- and software, simplicity and robustness have been key features of our work. The modular hardware design based on commodity parts was the basis of

8 an iterative improvement over the last three years. In terms of software, we developed a slim, custom software framework, accurate localization, and camera-based object detection. A special focus was laid on repeatability in case of changes in the environment, resulting in a particle filter-based localization algorithm with sophisticated feature extraction and an automatic color segmentation algorithm for object detection. With HANSE, we have participated at the Student Autonomous Underwater Vehicle Challenge in Europe (SAUC-E), winning the innovation prize in our first year of participation at Gosport, England in At the SAUC-E 2011 in La Spezia, Italy, we were able to achieve a great consistency throughout the entire competition, thus winning the first prize. In the future, as missions and therefore our algorithms are becoming more complex, the next step will be a migration to the Robot Operating System (ROS) framework, which will enable us to more easily incorporate advanced state-of-the-art algorithms. The expandable design will enable us to equip HANSE with new sensors, e.g. an acoustic modem, as needed to successfully compete in the years to come. References [ACL + 10] J. Aulinas, M. Carreras, X. Llado, J. Salvi, R. Garcia, R. Prados, and Y. Petillot. Feature extraction for underwater visual SLAM. In IEEE OCEANS, [APL + 11] J. Aulinas, Y. Petillot, X. Llado, J. Salvi, and R. Garcia. Vision-based underwater SLAM for the SPARUS AUV. In International Conference on Computer and IT Applications in the Maritime Industries (COMPIT), pages , [BB06] A. Boeing and T. Bräunl. SubSim: An autonomous underwater vehicle simulation package. In International Symposium on Autonomous Minirobots for Research and Edutainment (AMiRE), pages Springer Berlin Heidelberg, [Boh97] Harry Bohm. Build Your Own Underwater Robot and Other Wet Projects. Westcoast Words, January [BSA91] S. O. Belkasim, M. Shridhar, and M. Ahmadi. Pattern recognition with moment invariants: A comparative study and new results. Pattern Recognition, 24(12): , [FHLM11] D. Forouher, J. Hartmann, M. Litza, and E. Maehle. Sonar-based FastSLAM in an underwater environment using walls as features. In International Conference on Advanced Robotics (ICAR), [MCJ + 09] F. Maurelli, J. Cartwright, N. Johnson, G. Bossant, P.-L. Garmier, P. Regis, J. Sawas, and Y. Petillot. Nessie IV autonomous underwater vehicle. In Unmanned Underwater Vehicles Showcase (UUVS), [Ots79] N. Otsu. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man and Cybernetics, 9(1):62 66, [RBRC04] P. Ridao, E. Batlle, D. Ribas, and M. Carreras. NEPTUNE: A HIL simulator for multiple UUVs. In IEEE OCEANS, pages , [RRTN07] D. Ribas, P. Ridao, J.D. Tardos, and J. Neira. Underwater SLAM in a marina environment. In IEEE International Conference on Intelligent Robots and Systems (IROS), pages , [SD06] J. Sattar and G. Dudek. On the performance of color tracking algorithms for underwater robots under varying lighting and visibility. In IEEE International Conference on Robotics and Automation (ICRA), [SWL + 10] P.G.C.N. Senarathne, W. S. Wijesoma, Kwang Wee Lee, B. Kalyan, M.D.P Moratuwage, N.M. Patrikalakis, and F.S. Hover. MarineSIM : Robot simulation for marine environments. In IEEE OCEANS, 2010.