Figure 1: Block diagram representation of system design

Transcription

1 Proc. Of The 3rd World Congress on Computer Applications and Information Systems Autonomous UAV for Civil Defense Applications S.Basiratzadeh, M.Mir, M.T.Tahseen, A.D.Shapsough, M.S.Dorrikhteh, A.Saeidi Ajman University of Science & Technology, UAE Abstract This paper presents an autonomous Unmanned Aerial Vehicle developed for Civil Defense applications. It provides operational support to fire-fighters performing their mission in difficult indoor environments in high-rise buildings during loss of communication between fire-fighters and base station. It augments a 2-D indoor tracking system by providing the third dimension corresponding to the floor level where a fire-fighter is trapped. In this way, the position of a trapped fire-fighter in a high-rise building is completely identified. Other features of the developed system include the ability to recognize humans in poor visibility conditions resulting from smoke or at night, provision of a grappling trigger to carry extra load for rescue missions, and stable flight operation. Keywords UAV, Drone, Rescue operations, Fire-fighters, Tracking. I. INTRODUCTION Firefighting in indoor environments is a challenging task that is further complicated by factors such as loss of communication between fire-fighters and base station, limited visibility due to smoke or during night, and difficulty in tracking lost or unconscious fire-fighters in a high-rise building on fire. In order to ensure the safety of fire-fighters it is important that their position inside the building is known to the base station even during loss of communication as well as when they are unconscious. A number of indoor tracking systems have been developed for various applications using different technologies. These technologies include RFID [1,2], ultrasound-based systems [3], MEMS inertial sensors [4], and smartphone-based systems [5-7]. The smartphone-based system presented in [7] has been developed especially for tracking fire-fighters inside a building with no known map and without requiring installation of special hardware such as RFID tags. However, the system is effective only for 2-D tracking, that is, it can locate a fire-fighter on a building floor but cannot provide the floor number. For a truly effective system in high-rise buildings the third dimension (height of the fire-fighter or floor number) must also be provided to the base station so that the rescue operation could be carried out without loss of time. This can be achieved by using Unmanned Aerial Vehicle (UAV) or Drone presented in this paper. The demand of Unmanned Aerial Vehicles (UAVs) or Drones is steadily growing due to their high maneuverability and low cost in comparison to other solutions. The UAVs or Drones are currently deployed in a variety of fields such as military, aerial photography, civil defense, police, hospitals and industry and their most common applications are in monitoring and rescue operations [8-12]. For a detailed discussion on applications of unmanned aerial systems, the reader is referred to [13]. In this paper an autonomous UAV is developed to augment the 2-D indoor tracking system by providing the third dimension, that is, the height of the fire-fighter trapped in a high-rise building on fire. In addition, it provides backup communication when there is a loss of primary communication channel between fire-fighters and the base station. It also supports some other civil defense applications such as detection of humans in limited visibility due to smoke or in darkness, supplying a life tube to drowning person and quickly providing first-aid kits to accident victims where it is difficult to reach them by road. II. SYSTEM DESIGN The system design is explained with the help of a block diagram shown in Figure 1. The developed system comprises of three units: Multi-rotor unit, Kinect unit, and base-station unit. The Multi-rotor unit includes the UAV itself and flight controller modules for gathering and transmitting the required data (percentage of oxygen and carbon dioxide gasses, live video streaming during day or night using Kinect) and carrying essential items such as life tube and first-aid kit, etc. The Kinect unit includes the Kinect itself, a processor, and a transceiver. The base station unit includes a laptop with a Wi-Fi module with firmware installed for flight monitoring and a joystick for flight control in case manual flight is required. Kinect- Kinect, as illustrated in Figure 2, plays the role of system s eyes and ears. Kinect is used to gather and analyze data which are recorded during incidents in desert or smoke filled areas and will detect humans using depth information [14] during both day and night operations in order to rescue victims. The system not only uses depth information of Kinect but also a microphone array which is able to analyze sounds and indicate the direction [15]. In this way, even if the system cannot see vivtims, it can hear them and accordingly determine their location. One of the main advantages of using Kinect is that it is programmable which allows the system to be flexible for different applications. The other advantage is its low-cost in comparison to other solutions for human detection, 3D mapping, and microphone array. DOI: 08.WCCAIS N&N Global Technology 2016

2 Figure 1: Block diagram representation of system design On the other hand, Kinect has distance limitation for both video and audio analyses. To overcome this limitation, a small-size but high-quality camera (FatShark FPV) was also installed for covering longer distances exceeding 10m. Figure 2: Kinect Unit Drone- the system is based on a hexacopter which can take up to 9 kg load. This enables it to carry the system (windows embedded system, Kinect, power sources, drone s processing unit) and essential tools such as life tube or first-aid kit and to be stable during flight. The drone s processing unit is made of three parts: Microprocessor (ArduPilot Mega), GPS, and Telemetry. Its block diagram representation is given in Figure 3. APM (ArduPilot Mega) - is an Atmega based processing unit with all sensors (magnetometer, compass, accelerometer, barometer, 3-axis gyroscope) on-board for controlling the flight [8]. APM is a controller with open source environment which allows users to set their own parameters for different flight style missions and the low-cost processor allows the system to have stable and autonomous flight. APM is used for multirotors that utilizes 3S (11.1V) Li-Po battery. Since we were using 6S (22.1V) Li-Po battery, it was a challenge to utilize the APM with 6S battery for heavy multirotor and yet achieve stable flight operation. For this purpose we appropriately adjusted the PID controller of APM, as discussed later. Also, APM uses either 915MHZ in US or 433MHZ in European telemetry in order to transmit parameters obtained from APM, external compass, and GPS, as well as to communicate with base station. Using a dedicated frequency bandwidth to control the drone may cause a problem resulting in a crash due to high possibility of signal jammed by other communication devices which are using the same frequency bandwidth. For this reason, it is necessary to utilize a dedicated frequency other than commonly used standard frequencies. Windows Azure and Embedded System- The system is equipped with windows-based embedded system in order to analyze data from Kinect and transfer the desired video and audio after filtering motor noises or unclear area and sending them to base station (laptop, mobile, tablet) during mission operation. It is important to mention here that the purpose of the developed system is not just receiving and sending data or carrying tools but it is also designed to make a connection between emergency departments such as police, hospital, and civil defense with uploading the incident data using Windows Azure and be able to access it real time to avoid time delays. While the drone is flying it requires an internet connection for its embedded system and cloud processing. For lower altitudes, this connection is provided by the base station. Since this connection is lost at higher altitudes, an independent cellular modem (4G) was provided to support high-speed data transmission at high altitudes. Initially NUC and Raspberry Pi embedded systems were used but

3 they caused some communication and battery life problems. Finally, we decided to use Microsoft Surface Pro instead of embedded system due to its high processing power and longer flight time due to separation of its power source from drone source. barometer and gyroscope are not sufficient to achieve an optimimally stable flight. Therefore, a sonar sensor was added to achieve a stable flight of the developed drone. As shown in figure 5, altitude hold result (indicated by blue line) is showing reading of barometer (indicated by red line) and sonar (indicated by green line) to control the motors and maintain attitude during harsh weather conditions. Figure 5: Value graph showing barometer and sonar reading III. Figure 3: Drone s processing unit IMPLEMENTATION AND TESTING The designed autonomous UAV was implemented successfully as shown in figure 4. It was tested to evaluate its performance for specified operations. A proportional-integral-derivative (PID) controller is a control loop feedback mechanism commonly used in industrial control systems. A PID controller continuously calculates an "error value" as the difference between a measured process variable and a desired set point. The controller attempts to minimize the error over time by adjustment of a control variable. Figure 6 shows the interface of PID controller with APM. Figure 6: PID Platform Interface Figure 4: Testing of developed UAV During initial testing the UAV flight was not very stable. It was determined that the built-in or external sensors connected to the microprocessor such as accelerometer, The Mission Planner, shown in Figure 7, is a drone software having the following features: Point-and-click waypoint entry using Google Maps/Bing/Open street maps/custom WMS. Select mission commands from drop-down menus Download mission log files and analyze them Configure APM settings for the airframe

4 Interface with a PC flight simulator to create a full hardware-in-the-loop UAV simulator. Observe the output from APM s serial terminal and live video Figure 8: Kinect Application Figure 7: System Software IV. APPLICATIONS Indoor Positioning and Communication- Common problem among fire-fighters is losing communication between themselves and base station which may lead to disconnection with the base station or getting trapped in indoor building on fire. In case of loss of communication, the drone will fly outside the building and act as a repeater to establish communication between base station and fire-fighter. Also, it will search for signal coming from fire-fighter s RFID tag and determine their approximate location based on the signal strength. Rescue Mission- Provision of a grappling trigger allows the drone to carry extra payload for any rescue mission. It can carry a first-aid, a small fire extinguisher or a life tube for saving drowning persons. It is important to note that the drone can detect human body using Kinect IR sensor and reach a drowning victim faster than life guards. The developed system has been tested to drop a life tube and it was determined that, as compared to a life guard, the rescue operation by drone was four times faster when the victim was 100 meters away from sea shore. Human Detection and Smoke Visibility- As discussed earlier, the Kinect is used as human detector in both normal and low visibility conditions such as during night or in the presence smoke. A combination of IR emitter, color sensor, depth sensor, 4-array microphone, and a small motor, enables Kinect to perceive the nearby surroundings in 3-D, recognize humans, identify them, track them and understand their gestures, making Kinect an essential tool for enhancing the effectiveness of the system. A snapshot of the system s software is shown in Figure 8 that includes video transmission in day time, 3D modeling, voice recognition, and night vision. V. CHALLENGES FACED AND SOLUTIONS Battery Life- The most challenging part in drone implementation is limited battery life which is affected by several factors. Aerodynamic is one of the key factors to increase battery life by up to 40%. Centralizing the parts will also help drone to consume less power from motors and improve flight stability. Another factor which can increase the flight time by minimum 10 minutes is using light and strong material in body and propellers like carbon fiber which will cause less vibration in flight so that microprocessor and motors will consume less power with improved stability of flight operation. Locating the microprocessor on drone is also an important consideration in increasing the flight time. It can be located in X or + formation and during testing it was determined that the + formation will consume less power and result in more stability. Stabilizing Autopilot- Having autonomous flights is a challenge for all drones due to weather conditions, payload and drone design. For the presented drone, six motors were utilized in order to achieve stability in windy weather. While eight motors will further increase the stability, power consumption will increase and thereby the flight time will decrease. For the purpose of flight stability, the system is equipped with ultrasonic sensors so that data from sonar will interface with barometer, accelerometer, and gyroscope and provide useful data to UAV for a stable flight. Also, for a stable autonomous flight, the PID (Proportional-Integral-Derivative) should be tuned appropriately for proper operation of the motors. Communication Range - Ensuring communication between UAV and base station (laptop, mobile and tablet) was a challenge due to range limitation. This was partly overcome by using ad-hoc network in order to transmit video to mobile and laptop [18].

5 VI. CONCLUSION This paper has presented the design, implementation, and salient features of an autonomous UAV for civil defense applications. Along with a smartphone-based 2-D tracking system, the developed system can locate a trapped or unconscious fire-fighter in a high-rise building on fire even during loss of communication with the base station. The system has other applications as well such as carrying out rescue missions, detecting humans in poor visibility conditions, and establishing communication link between firefighters and the base station in case of loss of primary communication channel. The developed system has been tested for its various applications and in all cases it performed as expected. REFERENCES [1] M. Roberti, "Locating Firefighters With RFID", RFID Journal, Oct [2] Athalye, Akshay, et al. "Novel semi-passive RFID system for indoor localization." Sensors Journal, IEEE 13.2 (2013): [3] Gandhi, Siddhesh R. "A Real Time Indoor Navigation and Monitoring System for Firefighters and Visually Impaired." (2011). [4] Chowdhary, M., M. Jain, and R. K. Srivastava. "Test Results for Indoor Positioning Solution using MEMS Sensor Enabled GPS Receiver."Proceedings of the 23rd International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS 2010) [5] Lan, Kun-Chan, and Wen-Yuah Shih. "Using smart-phones and floor plans for indoor location tracking." Human-Machine Systems, IEEE Transactions on44.2 (2014): [6] GEOLOC, IFSTTAR. "Smartphone based gait analysis using STFT and wavelet transform for indoor navigation." International Conference on Indoor Positioning and Indoor Navigation. Vol System." Intelligent Systems for Crisis Management. Springer Berlin Heidelberg, *11+ Alec Momont, Ambulance drone' prototype unveiled in Holland, TU Delft, Holland, *12+ Nuryono S. Widodo, Anton Yudhana, Sunardi, Low Cost Open Source based UAV for Aerial Photography, International Journal of Innovative Research in Advanced Engineering, Nov [13] Odido, D., and Diana Madara. "Emerging Technologies: Use of Unmanned Aerial Systems in the Realisation of Vision 2030 Goals in the Counties." International Journal of Applied 3.8 (2013). [14] Xia, Lu, Chia-Chih Chen, and Jake K. Aggarwal. "Human detection using depth information by Kinect." Computer Vision and Pattern Recognition Workshops (CVPRW), 2011 IEEE Computer Society Conference on. IEEE, [15] Kumatani, Kenichi, et al. "Microphone array processing for distant speech recognition: Towards real-world deployment." Signal & Information Processing Association Annual Summit and Conference (APSIPA ASC), 2012 Asia-Pacific. IEEE, [16] Salih, Atheer L., et al. "Flight PID controller design for a UAV quadrotor." Scientific Research and Essays 5.23 (2010): [17] Ch. Bhanu Prakash, R. Srinu Naik, Tuning of PID Controller by Ziegler-Nichols Algorithm for Position Control of DC Motor, International Journal of Innovative Science, Engineering & Technology, May [18] Fife, Leslie D., and Le Gruenwald. "Research issues for data communication in mobile ad-hoc network database systems." ACM SIGMOD Record 32.2 (2003): [7] N. Nazir, N. Dawood, R. Karim, M. Younis, M. Mir, M. Dorrikhteh, S. Basiratzadeh, Smartphone-based Indoor Tracking System for Firefighters in Action, International Conference on Computer Technology and Information Systems (ICCTIS 2015), August 13-15, 2015, Dubai, UAE. [8] Lee, Seung-Jae, and Jong-Hwan Kim. "Development of a Quadrocoptor Robot with Vision and Ultrasonic Sensors for Distance Sensing and Mapping." Robot Intelligence Technology and Applications Springer Berlin Heidelberg, [9] Merino, Luis, et al. "Automatic forest fire monitoring and measurement using unmanned aerial vehicles." VI International Conference on Forest Fire Research DX Viegas (Ed.) [10] van Persie, Mark, et al. "Integration of Real-Time UAV Video into the Fire Brigades Crisis Management