an autonomous uav-ugv system for eradication of...

2017 Conference on Information and Communication Technology (CICT'17)

978-1-5386-1866-0/17/$31.00 ©2017 IEEE

An Autonomous UAV-UGV System for Eradication of Invasive Weed Prosopis juliflora

Balaji Baskaran, Karthikeyan Radhakrishnan, G Muthukrishnan, and R Nagendra Prasath

Department of Mechatronics Sri Krishna College of Engineering & Technology

Coimbatore, India

Abstract—Prosopis juliflora is a small weed tree, native to

Central and South America, and the Caribbean, which has become a major invasive weed in Asia, including India, Latin America, Africa and Australia. It has major detrimental impact on scarce, underground water resources, takes over the local flora ecosystems and affects the soil environment. Manual removal of saplings is time-consuming and expensive. Presently, the removal of the weed is performed manually which is time-consuming, or using very expensive commercial heavy equipment. In this paper, an affordable and innovative unmanned aerial vehicle (UAV) – unmanned ground vehicle (UGV) system is designed and developed for autonomous identification and removal of the weed seedlings and saplings. Results of implementation of a proof-of-concept prototype system using mainly commercial off-the-shelf components, open source hardware and free open source software, are presented to illustrate the effectiveness of the proposed approach.

Keywords— Prosopis juliflora; autonomous weed removal; unmanned aerial vehicle; unmanned ground vehicle; machine vision.

I. INTRODUCTION

Autonomous robotics is one of the disruptive technologies of our times, and has applications in a range of fields such as self-driving vehicles, Industrial Internet of Things (IIoT), precision agriculture, healthcare, oil and gas industry, and so on [1]. Given the importance to mankind of anthropomorphic Global Warming with its serious effects on farming and feeding a growing world population, precision farming has become a major emerging technology [2]. Precision agriculture has advantages of higher productivity, lower operating cost, improved safety, help with shortage of expensive and skilled labor, environment-friendliness (e.g., organic with use of less pesticides and weedicides), and so on. Some of the innovative technologies used by precision farming include wireless sensor networks, field robots, drones or unmanned aerial vehicles, sensors and IOT, analytics and cloud. As the increase in temperatures due to Global Warming, fall in rainfall (precipitation), and the resulting changes in soil quality over long-term lead to abundance of arable weeds, especially in arid and semi-arid conditions such as those prevalent in southern and north-western India [3].

Kamal P Balaji* and Shunmugham R Pandian, Senior Member, IEEE

*Teaching Learning Centre for Design & Manufacturing Department of Electronics Engineering

Indian Institute of Information Technology, Design & Manufacturing-Kancheepuram

Chennai, India

Prosopis juliflora is known in Hindi as angaraji babul, in Tamil as seemai karuvelam, in Telugu as mulla tumma, in Kannada as ballaari jaali, and so on [4]. Prosopis juliflora is known in Hindi as angaraji babul, in Tamil as seemai karuvelam, in Telugu as mulla tumma, in Kannada as ballaari jaali, and so on [4]. It is a shrub or small tree or mesquite, native to Central and South America, and the Caribbean. It was introduced in India in the 1950s as a source for fuelwood, farm barriers, and to fight desertification. However, over the past half century or so, it has become a major invasive weed in Asia, Arabia, Latin Africa, Africa, and Australia. Due to its serious ecological costs, it is called the Devil Tree in Africa, and Mad Tree in Gujarat.

Prosopis juliflora affects scarce, underground water resources by feeding on large quantities of water, increasing soil acidity, emission of CO2, taking over land from native flora, and so on. Some researchers have estimated that the weed consumes as much as 1000 liters of water to grow to weigh a kilogram, while other plants consume less than 50 liters [5]. Presently, the removal of the weed is done in many farming and rural communities in Asia and Africa using manual tools. However, this is a labor-intensive and time-consuming process. Occasionally, heavy equipment such as excavators are used for removal but the process is very costly, as the heavy equipment – even used ones - are often imported and expensive in terms of rent, fuel, maintenance, and labor. For example, the Tamil Nadu state Public Works Department estimated that in three southern districts alone, eradication of the weed over 208,000 hectares would cost more than eight billion Indian rupees (approximately, $126 million) [6].

In resource- and capital-poor countries of Asia and Africa, there is a need for more affordable and innovative technologies that can be developed indigenously using locally available materials and skills to fight the invasion of Prosopis juliflora. This approach is needed as the species tends to regrow unless the deep roots are also removed. Further, the weeds spread as their sweet seeds are consumed by cattle. Therefore, in this paper an innovative technology is designed and developed for automated identification of Prosopis juliflora seedlings and saplings over sustained periods of time using unmanned aerial vehicles as well as web/mobile app-based reporting tools. The


978-1-5386-1866-0/17/$31.00 ©2017 IEEE

geo-tagged images of the plant are then supplied to an unmanned ground vehicle fitted with cutting (and, stump grinding) tools for removing the weed in remotely operated or semi-autonomous and autonomous modes.

The technology is developed using inexpensive, commercial off-the-shelf (COTS) materials, open source hardware and free open source software (FOSS) technologies. It is suitable for adoption and adaptation on a wide scale, and can be further developed and refined using Citizen Science movements.

II. LITERATURE SURVEY

Prosopis juliflora is a sturdy weed or mesquite with high invasion rate due to a variety of factors: high germination nature of seeds (that can survive as long as 10 years), variety of mechanisms of seed dispersal (especially, cattle grazing), and ecological adaptability to differing arid and semi-arid environments [7]. Over long term, the invasion of the species has catastrophic effect on indigenous ecosystems as well as rural and farming communities [8], [9]. There are also interesting and unintended environmental consequences such as loss of habitats for birds and animals even in sanctuaries, e.g., [10], [11]. In recent years, an economic incentive for large scale eradication of Prosopis juliflora is sought to be provided in the form of biomass extraction from the weed [12], and its use for bioethanol and electricity generation [13], [14].

Precision agriculture is a multidisciplinary field encompassing use of electromechanical systems (field vehicles with/without on-board manipulators, drones, and other machinery), sensors, wireless networks, IOT, cloud and analytics for efficient management of farming, and specific and efficient application of seeds, fertilizers, pesticides, and water. In the context of the present research, there is also a shift away from inorganic farming that uses pesticides to which weeds are becoming resistant, to organic or mechanical tools for removal of weeds and superweeds [15].

Weed control has been a major part of precision agriculture technologies. In the earlier years, field vehicles equipped with on-board machine vision systems were used for identification and classification of the weeds, and site- and species-specific application of chemicals, e.g., [16], [17]. With the advent of low-cost unmanned aerial vehicles, the automated detection, classification and management of weeds over large areas has become cost-effective, e.g., [18], [19]. More recently, UAVs and UGVs are being used in coordinated planning, navigation, and operation for various applications like environmental surveillance and target tracking [20]. They have also found widespread use in precision agriculture, e.g., [21], [22].

Given the sheer scale and economic and ecological impact of the invasion of Prosopis juliflora in many arid and semi-arid states of India as well as countries in Asia, Africa and Latin America that are resource-poor, an effective and affordable multi-pronged approach is needed for automated or semi-automated identification and eradication of the invasive weed. Further, periodic monitoring for regrowth of seedlings and saplings is essential so that timely interventions can be useful in preventing further invasion with need for expensive

remediation. Therefore, in this paper, an integrated UAV-UGV system is proposed for autonomous identification and removal of Prosopis juliflora detection and removal.

III. UAV FOR IDENTIFICATION OF PROSOPIS JULIFLORA

The schematic of the proposed system for identification and removal of the invasive weed is shown in Fig. 1.

Figure 1. Schematic of proposed system

The system includes a database of locations of the weed in a neighborhood as reported by interested public using either a mobile application developed for smartphones and tablet PCs, or using a web application made available over the Internet. The information is reported either in terms of GPS (latitude and longitude) coordinates, or in terms of geographic location adjacent to landmarks. The administrator can login to view the database information. The public user can enter the complaint details in a form, entering optional details such as his/her name, contact phone number, along with the address, problem remarks, and landmark. The date and time of filing as well as GPS coordinates of the user will be auto-entered using the mobile app and the phone GPS. After the user enters the details and clicks submit, the values are stored in the database and later retrieved from the database to be displayed on the webpage to the administrator and other public administration (e.g., municipality) officials.

Alternately, since extensive or complete cataloging of the weed may be difficult manually and will be tedious, given a request from users in a certain locality, the drone can autonomously identify the weeds and map them over the entire locality using camera and GPS.

Figure 2. Quadcopter developed for weed identification


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Further, one or more UAVs can be used periodically to fly

over various areas where the weed dominates the landscape. The UAV – typically, a quadcopter – is equipped with a GPS and high-resolution camera, for recording videos of the weeds and their locations.

A photograph of the quadcopter developed is shown in Fig. 2 along with the system part names.

For ease of rapid prototyping and development, the quadcopter design is centered on Pixhawk an independent, open-hardware controller which provides high-end autopilot hardware and software. The autopilot module runs an efficient real-time operating system. Its main specifications are as follows:

CPU: 168 MHz Cortex M4F (256 KB RAM, 2 MB Flash) Sensors: 9-DOF IMU (accelerometer/gyroscope/ magneto-

meter) and a barometric pressure sensor Integrated backup, override and failsafe processor with

mixing MicroSD slot, 5 UARTs, CAN, I2C, SPI, ADC, etc. Essentially, the autopilot receives a signal from the radio

receiver in remote control mode. The Pixhawk controls the flight by the help of various sensors like accelerometer, magnetometer, barometer, and gyroscope using a built-in PID control algorithm. The architecture of the UAV is illustrated in Fig. 3 in the form of a block diagram:

Figure 3. Architecture of quadcopter system

The telemetry subsystem is used to send the data collected

from the controller to the ground station, and operates at 433 MHz. A buzzer is also connected to the Pixhawk which indicates to the user whether the device is connected and it also sends an alarm when the flight mode is changed.

A 720p HD video camera (webcam) is used to take high definition photos and videos when the UAV is in flight. The camera is connected to a Raspberry Pi 3 microcomputer

through USB. The camera is controlled by the RPi and the videos and photos taken by the camera are stored in SD card.

A GPS logger shield provides the Arduino microcontroller with access to a GPS module, µSD memory card, and all of the other peripherals. The shield is based on a 66-channel GPS receiver and has an update rate up to 10Hz. The GPS module will stream constant position updates over a simple TTL-level serial port, which then can be logged to the µSD card for real-time or offline image processing.

The RPi is enabled when the UAV is in the takeoff mode. The camera begins to record when the arming is done in the UAV. While receiving the video, the RPi also receives the GPS data from the Arduino through serial communication. Using Python code, the RPi directly prints the GPS coordinate data on the video. Thus, the geotagging video is recorded in the RPi’s SD card storage.

The RPi also provides template matching options using OpenCV computer vision software installed in the RPi. It initially separates the video into a sequence of image frames. Each frame is then matched with multiple templates of the weed to identify the weed. If the template is matched with the frame, then the GPS coordinates in that matched frame are stored separately in RPi in a database of the weed locations in a neighborhood. This database is passed on to the control computer of the UGV in real-time or offline.

Figure 4. Coordinates of quadcopter system The GPS location of the weed is then found using the

current position of the UAV which is displayed in the geotagged image, from the bearing angle of the camera θb and the altitude A of UAV from ground, as shown in Fig. 4.


978-1-5386-1866-0/17/$31.00 ©2017 IEEE

The altitude is calculated by the barometer and temperature sensors. The altitude measurement is typically reliable above a height of about 2 to 3 meters. The altitude is found as [26] = | where c is a constant depending on the temperatures, T is temperature, Po is the air pressure at sea level, P is the air pressure at UAV altitude A. The bearing angle of the camera is entered by the user as the camera mounting angle is fixed. The Lock distance (D) is determined as

D = Tan(A)/6378.137 where the empirical constant in the denominator accounts for radius of earth in km. To find the GPS locations of the weed, we initially convert all the values from degrees to radians. Then, the latitude and longitude are found using the formulas below:

cL =asin (sin (aL) *cos (D)) + (cos (aL) *sin (D) *cos ( )) cO = aO + atan2((cos (D)-sin (aL) *sin (cL)), sin ( ) *sin

((D) *cos (aL)) where, aL - latitude of UAV, cL - latitude of weed, aO - longitude of UAV, cO - longitude of weed, A - altitude of UAV from ground, D - lock distance, - bearing angle of camera. These coordinates of weed can be transferred from the UAV to the UGV either in real-time or offline after each trip. The telemetry subsystem used with the UAV has a range of 2km.

IV. UGV FOR REMOVAL OF PROSOPIS JULIFLORA

A schematic of the UGV for weed removal is shown in Fig. 5.

Figure 5. Schematic of UGV system

To reduce costs, the prototype weed cutter was made by cutting and shaping a wooden (plywood) board of thickness 20mm. The middle position of the board was removed to mount the engine, from which the cutting power is transmitted to a pulley. The shaft was made of hardened steel and it was

machined to fit the inner knurl of the engine transmission assembly. A separate wooden piece was fitted to the front end and the bearing housing was fitted onto the wooden part. The housing was made to hold the shaft to which the driven pulley is mounted.

The motor was mounted near front part through drill and assembly. The rear wheels are chosen as caster wheels so the UGV can move in any direction provided that they follow the movement of the wheels connected to the motor. The shaft whose one side was provided with an inner thread is connected to the motor shaft and the other side was connected to the wheels which transmit the rotational force to them.

The sleeves were made to hold the engine at a distance from the wooden board and to reduce the vibration being transferred to the base assembly. Calculating the distance between the pulleys, a suitable V-belt was selected for power transmission.

The petrol engine is the main part of the cutting system which provides the mechanical energy. Power is transferred through the belt-pulley transmission system. The engine used here is a 52cc brush cutter petrol engine which provides 1.7kW power output. The final part of the system is a saw blade which cuts the plant/weed stems. The engine on/off control is done through a relay which is operated by the Arduino. A schematic of the UGV weed cutter is shown in Fig. 6.

Figure 6. 3D view of the UGV weed cutter The Arduino control program is uploaded through USB

interface and the signal from the Arduino controls the motor drivers. After this, the USB that connects the Arduino with HD camera is set up. A kill switch is mounted near the engine which can be used in the case where the connection is lost.

The Arduino is used to control the DC motors and the engine and receives the GPS coordinates from the user (offline) or the UAV (real-time). The autonomous movement of the UGV is guided by the navigation sensors LIDAR and IMU. The heading angle of the UGV is given by the IMU. The IMU consists of a three-axis angular rate sensor, a three-axis digital compass, and a three- axis accelerometer. The LIDAR


978-1-5386-1866-0/17/$31.00 ©2017 IEEE

is used to detect target weeds and any obstacles in real-time and to plan or modify the path for target tracking and obstacle avoidance.

Figure 7. Estimation of UGV bearing angle from GPS location

Here, we use the north 0° as home position and then the UGV calculates the heading angle by using the current GPS coordinates and the target position. The bearing angle is calculated from the formulas [24]

X = cos * sin Δ Y = cos * sin – sin * cos * cos∆

To find the true bearing angle

β = atan2(X, Y) where,

- latitude of UGV, ∆ - difference in the longitudes of UGV and weed, - latitude of weed, and β – bearing angle. Now, the UGV will turn to true bearing angle and it tries to move along the same angle until it reaches its destination (Fig. 7).

Once, the data is received from the user, the Arduino then commands the motor controllers to start the motors. The LIDAR and IMU sensors are used to navigate the UGV via a sequence of waypoints in a contiguous locality, to the vicinity of the GPS position of each weed in a closed or open path, while bypassing the obstacles using real-time vision feedback from the on-board camera. The camera is controlled by the on-board RPi with OpenCV installed. The RPi then takes photos/videos of the trees nearby. It then compares the video with multiple stored templates of the Prosopis juliflora weed. If the matching done is true then the RPi commands the Arduino to start the engine. The engine is the propeller unit which is used to cut the tree using the saw blade with high speed and torque. After cutting the weed down, the UGV is directed to the next GPS coordinate along the open/closed path. For convenience, the path is developed using OkMap cartographic software for tracking and navigation.

V. RESULTS AND DISCUSSIONS

The prototype UAV was used to map the Prosopis juliflora in a neighborhood, as shown in Fig. 8.

Figure 8. UAV flying over weeds for mapping The Pixhawk development environment makes it easier to

plan and navigate along specified trajectories, using the supplied Mission Planner software.

A typical aerial image of the weeds taken by the UAV with geotagged information is given in Fig. 9.

Figure 9. Geotagged image of weed from UAV Finally, a photograph of the working prototype cutting

down a weed sapling is illustrated in Fig. 10.

Figure 10. Photograph of UGV cutting a weed sapling


978-1-5386-1866-0/17/$31.00 ©2017 IEEE

The cost of fabrication of the prototype UGV is about

30,900 Indian rupees (less than USD 500). The cost of in-house labor is not included.

The total cost of the UAV is approximately 37,100 Indian rupees (less than USD 600). The total cost of the UAV-UGV system thus amounts to about 68,000 Indian rupees (less than USD 1,100).

The present project offers significant scope for innovation and societal impact. New or used engines with more power capability can be used to fabricate gradually larger and more powerful weed cutting machines at affordable cost, similar to the crowd-sourced and open hardware farming and other tools in [23]. Additional add-on tools like stump grinder (for root removal) and wood chipper can also be added to the cutting machine, so that a self-contained set of machines can be deployed in both semi-autonomous and autonomous modes.

VI. CONCLUSIONS

In this paper, an innovative and low-cost integrated UAV-UGV system for semi-autonomous (remote controlled) and autonomous identification and cutting of the invasive weed Prosopis juliflora has been designed, developed, and successfully tested. The technology is suitable for dissemination and adaptation on a large scale. In future work, it is planned to outfit the UGV with more and more powerful engines, and also add accessories such as stump grinder and chipper or cutting up of the removed weeds.

ACKNOWLEDGMENT

The funding for this research was facilitated by the Teaching Learning Centre for Design and Manufacturing Education at Indian Institute of Information Technology, Design and Manufacturing-Kancheepuram. The assistance of Mr Subramanian Rajaganapathi in the mechanical design is acknowledged.

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