Written by Arjuna Balasuriya, Sardha Wijesoma, Bharath Kalyan, Thomas Lim
Saturday, 04 July 2009
1. Introduction
In order to explore the vaguely understood, wide and deep underwater environment, which covers two thirds of our planet, it is necessary to navigate to different locations [4]. {mosgoogle}The importance of underwater exploration lies on the fact that the underwater environment is rich with hard mineral and renewable resources, and oil/gas reserves.
Current underwater operations are carried out by human divers, manned submersibles, towed systems and by remotely operated vehicles (ROVs). Operations using these systems introduce high risk, high cost and require dedicated surface support. The tether connected to the surface restricts the maneuverability of the mobile systems. Thus, there is a high demand for mobile systems, which do not depend on surface support and the tether. These unmanned untethered vehicles are called Autonomous Underwater Vehicles (AUVs). AUVs have the potential to revolutionize access to the deep and shallow underwater environment and address critical problems such as search and mapping, climate change assessment, life form behavior monitoring, mine countermeasures etc [2,3,5,6,7]. Therefore it is important that these AUVs are capable of reacting intelligently to unknown changes in the environment and navigate to desired locations with minimum support from the surface (human intervention).
Navigation therefore, is one of the primary challenges in AUV research today. This is true for any autonomous mobile system. However, underwater systems are more challenging due to the complex and hostile nature of the environment and due to lack of reliable sensory systems available for underwater operations [6]. Reliable navigation information is essential for safe operation and recovery of an AUV.
At present, most of the AUVs are application oriented and are employed in known environments [3,7]. They still depend on some support from a surface vessel or on ground fixed transponder networks for localization. In order to commercialize an AUV, it should be a costeffective alternative to other available technologies, such as manned submersibles, ROVs and towed systems. The benefits offered by autonomous data gathering have to be weighed against the difficulties faced by AUVs in power, sensing, information processing, navigation and control.
These challenges lead to research and development activities in the areas of AUV navigation, path planning, sensing, power supplies, information processing, hardware/software architectures, and in controls. It is therefore necessary to have a test-bed facility in which these new technologies, methods, algorithms can be tested in real world conditions.
This paper discusses the test-bed AUV platform designed and developed by NTU called NTUUAV. Its hardware/software architecture is reconfigurable enabling the researchers to change the placement of the sensors and actuators for testing different navigation scenarios. Also the hardware/software components are re-usable. This reduces the coding, debugging and testing time.
2. The NTU-UAV The low-cost (S$200K) Nanyang Technological University Underwater Autonomous Vehicle (NTU-UAV) has 4 Degrees-of-freedom (DOF) using four-propeller thrusters as shown in Fig. 1. There are two forward thrusters for surging and yawing and two other thrusters for heaving and rolling. However the NTU-UAV is not actuated for rolling. The vehicle is trimmed in such a way that the rolling and pitching effects are kept to a minimum. The dry-weight of the vehicle is 250kg and is neutrally buoyant. The NTU-UAV carries on-board sensors, which can receive the INS information, bottom and water tracking speeds of the vehicle, optical images, sonar images, range to obstacles and to the bottom, and diving depth.
Figure 1. The NTU-UAV
2.1. Internal Sensors The internal sensors provide the motion parameters of the vehicle with respect to vehicle’s coordinate system. The Motion Reference Unit (MRU) measures the linear motion and the attitude of the AUV [13]. The Seatex MRU-6 is equipped with 3- axis accelerometers, angular rate sensors and a magnetometer. The MRU measure roll, pitch, yaw angles, and heave, surge and sway accelerations. The raw data captured from the sensor head of the MRU is further processed using a 68332 computing unit [13]. The interface to the MRU is through 4 analogue channels or serial RS232.
Three Tritech altimeters detect the distance to the objects in the environment [8]. These acoustic range finders will be useful in identifying obstacles in the path of the vehicle. The Tritech Miniking Sonar is mounted on the NTU-UAV for obstacle avoidance and sonar imaging [8]. The scanning sonar provides the range and bearing to the environmental objects in a 2D plane. The CCD camera mounted in front of the AUV provides information for inspection, short-range target identification/tracking and for docking purposes [8].
2.3. Actuators The NTU-UAV has 4 DOF using four propeller thrusters. Tecnadyne 250 model thrusters are mounted for the surging and yawing motion while 520 model thrusters are mounted vertically for heaving and rolling motion [9]. The amplifier circuits are housed within the motor case making the implementation simple. The propellers are magnetically coupled.
2.4. Information Processing Units
The cross bow Attitude Heading Reference System (AHRS) is equipped with linear accelerometers, rotational rate sensors, and magnetometers [12]. The AHRS data can be captured via analogue and RS232 interfaces. The precision navigation system’s TCM-2 electronic compass uses inclinometers to capture the state of the roll, pitch and heading angle with respect to the magnetic north [10]. The three internal sensors provide 3-D linear acceleration, angular speeds and angle positions in the vehicle coordinate frame.
2.2. External Sensors A Druck PTX 1830 pressure sensor measures the external pressure experienced by the vehicle and is used to measure the depth [15]. This sensor provides a voltage proportional to the pressure. The data can be retrieved using an analogue interface.
The speed of the vehicle with respect to the bottom or water can be measured using the Argonaut Acoustic Doppler SpeedLog [14]. This sensor also provides the range to the bottom and estimates the distance traveled in X and Y directions. The processed data is sent through a RS232 serial link.
Capturing data from each sensor and processing them to extract useful information is a very important issue in autonomous mobile systems. The NTU-UAV has three processing units connected in a LAN as shown in Fig. 2.
Figure 2. Schematic View of the Signal Flow
The internal component arrangement is shown in Fig. 3.
Figure 3. Arrangements of the Internal Components
CPU1 and CPU2 uses the Prometheus PC/104 embedded system with Ethernet and other IO controllers [11]. Each CPU unit has 4-RS232 ports, 2-USB ports, parallel port, 100BaseT Ethernet port, 16-Ch ADC, 4-Ch DAC, and 24-DIO. The IP7500 is used as the CPU3 mainly for image capturing and signal processing. CPU3 is a Hitachi SuperRISC CPU SH-4. CPU1 and CPU2 use the RT-Linux operating system (OS) while CPU3 uses the RedHat Linux OS.
2.5. Power Supply We used Yardney’s Silvercel LR190DC rechargeable cells [16]. It is a compact, silver-zinc alkaline cell. Its nominal capacity is 135 AH. The batteries provide 24V DC and the power distribution circuit distributes power to each device through a fuse box arrangement for the safe operation of the devices. The power supply is designed to provide a 1-hour endurance time to the NTU-UAV.
2.6. The Pressure Hull The aluminum hull and the connectors are designed for a depth of 100m. For easy handling, trimming weights are removed on surface. The trimming weights (150kg) are added to make the system neutrally buoyant.
3. Software Architecture A test-bed system features - 1) hardware/software should be reconfigurable to suit a variety of applications. 2) programming environment should be user friendly so that researchers can implement their ideas with minimum coding. Example: researcher working on sensor fusion need not spend time programming the interfacing routines, control routines for the AUV. Therefore it is important that the low-level hardware/software modules are developed so as to permit reusability by higherlevel software objects. We developed a four-layered software framework as shown in Fig. 4. The application layer is the top layer in the framework, which provides the developer or researcher to represent his/her idea.
The application can be designed from instantiating objects from the lower-level layers of the framework. The primitive behaviors are identified for the AUV system, which can be called depending on the requirement of the application.
Figure 4. The Layered Framework
The Application Development Environment (ADE) layer gives the actual abstraction to the higher layer for developing mission specific applications. The ADE layer uses the object-oriented concepts to present the low-level data to the high-level application layer. Software objects are designed to read the state of the vehicle and command actuators.
The goal of the third layer (Real-Time Hardware Interface Layer) is to provide hardware extraction capabilities. The data from devices are captured in real-time, accessed in parallel and synchronized from different processors. The kernel of the operating system provides the major interface between the basic devices and the software modules. This layer performs primitive functions such as data capturing from sensors and commanding actuators. Data processing modules for each of the devices and for communication modules are developed using threads. Thus greater flexibility in reconfiguration is achieved through handling respective hardware/software components. The Hardware Layer consists of the sensors, actuators, processors, communication systems, and power systems. The device arrangement in this layer is shown in Fig. 2.
4. Experimental Results The first set of experiments was conducted at the NTU swimming facility for determining trimming characteristics (in roll/pitch) and identifying the surge model of the NTU-UAV. Iron bars (total weight of 150kg) were placed on the frame, which is below the pressure hull, to make the NTU-UAV neutrally buoyant as shown in Fig. 5.
Figure 5. NTU-UAV After Trimming
The NTU-UAV was actuated in the surge direction using the forward thrusters and the status of the vehicle was captured using the MRU and DVL. Based on the applied force and captured status information, a parameter estimation was done to the simplified dynamic equation of motion in surge as given in Eq. 1 [1].
Where, u is the surge speed and τ is the surge force applied to the forward thrusters.
5. Conclusions The School of EEE, NTU has designed and developed a test-bed AUV for conducting research in the area of underwater navigation, path planning, sensor fusion, target identification, target tracking, localization, mapping etc. The NTU-UAV has a modular hardware/software architecture, which will enable researchers to configure the architecture depending on their requirements. The implementation of research ideas on the test-bed platform can be carried out in a short time using the re-usable software components developed based on the object-oriented methods.
Acknowledgements Authors would like to thank other members of the AcRF RG6/01 team who contributed to the development of the NTU-UAV.
References [1] R.Q Ang and Y.K. Lee, System Identification of an Unmanned Untethered Underwater Vehicle (UUUV), FYP report 4004, EEE, NTU, 2003. [2] A. Balasuriya and L. Cong, Adaptive Fuzzy Sliding Mode Controller for Autonomous Underwater Vehicles, Proc. of the 4th Int. Conf. on Control and Automation, Canada, June 2003. [3] A. Balasuriya and T. Ura, Vision Based Underwater Cable Detection and Following using AUVs, Proc. of IEEE Oceans, Biloxi, October 2002. [4] J. J. Leonard, A. A. Bennett, C. M. Smith, H. J. S. Feder, Autonomous Underwater Vehicle Navigation, MIT Marine Robotics Laboratory Technical Memorandum 98-1. [5] J. Rosenblatt, S. Williams and D.W. Hugh, A Behavior-based Architecture for Autonomous Underwater Exploration, Information Sciences 145, 2002 [6] J. Yuh, Design and Control of Autonomous Underwater Robots: A Survey, Autonomous Robots 8, 7-24, 2000 [7] T. Ura, Development of Autonomous Underwater Vehicles in Japan, Journal of Advanced Robotics, Vol. 16, no. 1, pp 3-15, 2002 [8] http://www.tritech.co.uk/ [9] http://www.tecnadyne.com/thrusters.htm [10] http://www.precisionnav.com [11] http://www.diamondsystems.com [12] http://www.xbow.com [13] http://www.seatex.no [14] http://www.sontek.com [15] http://www.druck.com [16] http://www.yardney.com
1. Introduction
