Developing a field robotic test platform using Lego®Mindstorm®NXT Gareth Edwards*,1; Dionysis D Bochtis1; Claus G. Sørensen1 1
University of Aarhus, Department of Engineering, Blichers Allé 20, 8830 Tjele, Denmark *Corresponding author. E-mail:
[email protected]
Abstract The testing of different systems, methods and planning approaches for in-field operations and management practises associated with agricultural machinery can be both costly and time-consuming. Normally, such systems can be developed and tested using computer simulations. However, in order to gain an understanding of real-world performance there must be an intermediate step between computer simulation and full scale testing. To this end, using test platforms can implement different methods of operational and management practises on a scale model. In this paper, a Lego®mindstorms®NXT based platform including a micro-tractor and a mounted implement is described and an example of real-time mapping of spatial variance is used to demonstrate its functionality. The platform is evaluated by its ability to follow the coverage plan. Keywords: field robots; in-door simulation; micro-tractor; B-patterns; area coverage. 1.
Introduction
In the case of precision farming, the knowledge of the variability within the working environment is very important. For example, the spatial variability of the work area in operations management will play an important role as input to the decision making process and the efforts to increase the efficiency. Many different quantitative properties of both the soil and crops, including soil texture, organic matter content, water retention, crop height, weed density, etc., could be measured and visualized on a map. Particularly, electro magnetic (EM) readings of the soil have been shown as a good predictor for the variability of clay and water in many soil types (Sudduth et al., 2005; Corwin & Lesch, 2005), and can be used for the generation of soil textural maps which can relate to trafficability (K. Saffih-Hdadi et al. 2009) or crop yield (Nevens & Reheul, 2003). Automatic weeding operations can also benefit significantly from increased knowledge of the spatial distribution of the weed patches. Targeting weeding operations involve taking images of the crop, processing the images, and then actuating based upon whether weeds are detected or not. This can limit the amount of herbicide used, save money and increase the yield of the crop (Christensen et al, 2009). Nevertheless, the development of an integrated real-time mapping system is a complex process. To this end, the application and use of test platforms is seen as promising as an important part of the development process, so that real-life mapping technologies and operational approaches can be simulated and examined on a small scale. In this paper, a Lego®mindstorms®NXT micro-tractor platform will be described in terms of its hardware and software components as a complete small scale system for any field
mapping operation showing how the detected results can be plotted in real time for use in additional operations. 2.
Materials and methods
Lego Mindstorm has been used as a framework in a number of scientific areas involving the development of robotic systems such as, robotic exploitation (T. Kovács et al., 2011) and team intelligence (Simonin & Grunder, 2009). By providing an accessible and versatile framework, Lego Mindstorm is extremely useful for rapid prototyping of mechanical automated systems which can be programmed with a high level of complexity. The general functionality of Lego also allows for the addition or reconfiguration of the systems architecture, so that the platform can be adapted for a number of different applications. Lego Mindstorm is a suite developed by Lego®. The main controlling unit of the suite is referred to as the “NXT Intelligent Brick”. It is programmed either using Lego’s own Mindstorm IDE (Integrated development environment) or various third-party development tools. The NXT Brick has connection ports for 7 RJ12 cables and is capable of controlling 3 Lego NXT servo motors and receive inputs from 4 sensors. The Lego NXT motors can be set to rotate in a given direction with a given power, or controlled incrementally using built-in rotary encoders which can be stepped with an accuracy of 0.5 degrees. The Lego sensors offer basic measurements such as light sensors, touch sensors and ultra-sonic sensors, however third-party companies (e.g., ViTech, Microinfinity, Dexter Industries) have developed additional sensors, such as temperature sensors, colour sensors, chemical sensors. The sensors connections can be either an analogue signal or serial signal using an I2C bus. 2.1 The Micro-Tractor
(a)
(b)
Figure. 1: Photos of the steering and drive components By examining typical dimensions of medium sized tractors (of around 150 hp) the wheelbase and turning radius of the micro-tractor were set at 175 mm and 370 mm, respectively. This is an average scale ratio of 1:14, corresponding to a tractor with wheelbase of 2.45 m and turning radius of 5.18 m. The micro-tractor’s steering is controlled by an NXT servo motor, which is geared down with a ratio of approximately 7:1, (Figure 1a). The actuation of the wheel is provided via a rack and pinion system which allows for a range of movement of the wheels of +/- 30o. The micro-tractor’s drive is also controlled by an NXT servo motor, which provides power to the rear wheels via a differential gear (Figure 1b), allowing the micro-tractor to navigate turns with minimal wheel slip. The combination of the rear gearing ratio and the
rear wheel diameter means that for each degree turned by the drive motor, the centre of the rear axle is moved 0.51mm The main sensory input is from the CruizCore® XG1300L inertial measurement unit (IMU), which is used to provide the heading measurement relative to the micro-tractor’s starting position. . The IMU processes the signals from a single axis MEMS gyroscope and a three axis accelerometer, and applies factory set compensation factors, to output a value of degrees turned away from the initial heading (Microinfinity Co., Ltd. 2011). 2.2
The mapping implement
Figure. 2: Photo of the implement An implement was constructed to demonstrate some of the operational functionalities of an automated crop sprayer. The implement, (Figure 2), consisted of an NXT Brick for data processing and communication, and four Lego colour sensors. Three additional motors were also built into the implement, one to provide steering to the implement’s wheels via a rack and pinion, and two for operational functionality such as raising and extending the sprayer boom, although these were not utilised in the current experiment. The colour sensors are active sensors, measuring the light reflected from the surface below them and transmitting a reference number to the NXT Brick. The four sensors were spaced out along the rear of the implement to give a total coverage width of 250mm. The reading from each sensor is passed through a threshold filter to transform it into a binary state signal of “on” if the sensor is over a specified colour and “off” at all other times. The implement also emits a tone if any of the sensors is in an “on” state, as an indication to an observer. This tone could also be a representation of a physical actuation, such as a sprayer nozzle opening or the lowering of a subsoil plough. The principals of the implements operation could be applied to a number of real world systems which collect information about the physical environment and use this data to construct a map. Once this map has been generated, addition operations can be planned and optimised. 2.3
Software
BricxCC (Bricx Command Centre), an open source Windows program which uses NXC programming language, is used to compile the programs contained on the NXT Brick.
Matlab (MathWorks®), and the RWTH – Mindstorm NXT toolbox were used for the remotely communication with the NXT Brick via Bluetooth. 2.3.1 Position determination The micro-tractor’s position is determined using the calculated heading from the onboard IMU and encoder on the drive motor. The position is calculated both by the NXT brick of the micro-tractor, for use in course correction, and by the laptop, for the visualisation. The accuracy of this method of position determination was validated using an in-door GPS (iGPS), (Nikon Metrology, NV Europe). The average error between the estimated position and the position from the iGPS was over a 75m route was 0.048m, which was deemed to be acceptable for demonstrative purposes. 2.3.2 Route planning Matlab was also used for the route planning of the micro-tractor. Using the boundary of the working area and parameters of the operation, micro-tractor and implement, a plan detailing the headland passes and the working rows was generated, configured to provide good coverage of the working area. The sequence in which the rows were traversed was optimised following the principle of B-patterns, that is algorithmically resulting optimal track traversal sequence according to an optimisation criterion (Bochtis et al., 2009; Bochtis & Sørensen, 2009). The route was compiled into one continuous path and then converted into a series of straight lines and turns and written into a text file to be copied to the NAC program. The straight line segments are described by the heading, distance, speed, and the starting point coordinates. Turning segments are described by the initial and final heading, the speed and the direction of the turn. 2.3.3 Vehicle control The control algorithms programmed directly onto the NXT Brick translate the orders calculated by the route planning method into direct commands of the micro-tractor’s motors. A feedback loop is initialised which checks and corrects errors in the microtractor desired position against its calculated position. While control of the motors could be maintained directly from the Matlab program, via commands issued over the Bluetooth protocol, the procedure of having the calculations processed on the NXT Brick allows for a quicker refresh rate and more accurate control. Although corrections are executed in order to maintain the prescribed route, the route cannot change during an operation. 2.3.4 Visualisation Data from both the micro-tractor and the implement are transmitted over the Bluetooth protocol, received by the laptop computer and processed in Matlab. The micro-tractor transmits the encoder value of the drive motor and the output of the heading sensor, while the implement transmits the binary signal for each sensor. The refresh rate of the position determination is based on the frequency at which the data is transmitted from the NXT Bricks, and set at around 100ms. A presentation of the field polygon is shown on screen and the real time position of the micro-tractor and implement is plotted from the data from the micro-tractor. When a sensor on the implement indicates that it is positioned a specified colour, then a mark is generated on the real time map. In this way, using a coverage route plan for the tractor, the whole field can be mapped out, with highlighted areas of interest.
Figure 3: Mapping visualisation, the red line is the prescribed route, the blue line is the path taken, and the orange dots are points identified by the implement 3.
Demonstration of the micro-tractor and implement
A field 3.5m x 5m was chosen as a standard for demonstration purposes, as this is a sufficient size to demonstrate the ideas of coverage route planning, while still small enough to be transportable. An area of 0.8 m x 1 m in the corner of the field was designated as a farm yard to make the shape of the field not simply a square. Red patches of paper where randomly placed around the field to be detected by the implement, and marked on the map. The highlighted areas of interest has been visualised as shown in Figure. 3. Figure. 4 shows the coverage of the work area by the implement from a single test run, while the results shown in Table 1 show are averages from 6 test runs.
Figure 4: Area Coverage. Green indicates area covered, while black indicates missed area Table 1: Averaged results from six test runs with the same setup Working Width (mm) 250
Actual Expected Path Path Length Length Angle (m) (m) 90 73.76 74.68
Path Length error (%) 1.24%
Cross Work Area path error covered (mm) (%) 22.53 91.58
4.
Conclusions
The Lego®Mindstorm®NXT has been used as the basis for developing a micro-tractor and a field area mapping implement. The micro-tractor and the implement are able to offer good coverage of the area while using a small working width, and display the results effectively to the user in real time. The whole system provides an extendable general-purpose platform where a number of different applications can be simulated and tested within in-door small scale confinements. This feature makes the platform an important and integrated part of the development process of a system, method, or planning approaches for both scientific and educational purposes. 5.
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