計測自動制御学会東北支部 第 335 回研究集会 (2021.10.29)
資料番号 335-4

Conversion of a Commercial Electric Cart into a Mobile
Platform for Agriculture
Paula Costa do Valle Gondim ∗ , Yuichi Tsumaki∗*Yamagata University
Keywords : agricultural robot, mobile platform, electric cart, external encoder, modular design
Contact Information : Dept. of Mechanical Systems Eng., Yamaga University, 4-3-16 Jonan,
Yonezawa

Zip-Code: 992-8510

Tel.: 0238-26-3736, E-mail: paulacvalle@gmail.com

1.

Introduction

crop called “Yamagata Cherry,” a delicate red

In the last two decades the world has seenan increase in academic papers, technologies
and companies related to applying digital tools
to improve crop production, quality and harvest time, as well as reducing costs. DigitalFarming has been increasingly implemented
into agricultural practices due to the advancescherry that must be harvested along with its
stem. Harvesting this crop can be demanding
to the body of farmers, since every cherry mustbe hand picked.
In an eﬀort to help farmers with this task,
we are developing a robot capable of delicately
harvesting this crop 6) .
To successfully develop a fully automatedin technologies such as wireless communication, cloud storage and data analytics 1) 2) .

cherry harvesting robot, there are a few im-Japan is no exception, and as farmers’
average age increases 3) both the government

recognition of cherries for harvest, the ability

and companies look for alternatives to facilitate agricultural tasks, such as crop harvestingand seed sowing.
These tasks are not necessarily complex forhumans, even if they are time consuming andexhaustive, but they can pose diﬃcult problems for robots due to environments diﬃcult
to traverse, with repetitive patterns, irregularsoil, small or delicate crops, etc 4) 5) .
In Yamagata prefecture, there is a special

portant milestones to be achieved, such as: theto precisely remove the cherries from the tree
branches without damaging them and the ability to navigate inside the cherry orchard safely.
Although the task of harvesting cherries isundeniably challenging, such a robot cannot
achieve fully automated harvest without amobile platform capable of driving itself
autonomously inside a cherry orchard.
This platform needs to be able to traverse
an irregular terrain, identify and avoid obsta-–1–

tains a rechargeable 24 V battery pack, fourwheels, on/oﬀ key-switch, and it has the conﬁguration of front-wheel-steering and backwheel-driving, controlled by a steering handlebar and an accelerator lever. The maximum
load that can be carried is 200 kg, at a maximum speed of 4.5 km/h forward and 2.9 km/h
backwards. The main motor is a 24 V DC,
350 W brushless transaxle motor coupled tothe back wheels axis.
There are two main reasons as to why a commercial cart conversion was done: time saving
and cost management.
Converting a premade cart was a simpler
Fig. 1 Cherry Harvesting Robot, developedby the Telerobotics Laboratory at Yamagata
University.

task than designing and manufacturing a cartcles, and carry the heavy load of all integrated

Meanwhile, when considering the costs of de-body, possibly saving months of prototype development.
signing and manufacturing a very similar cart

robotic equipment.
The cherry harvesting robot from the Teler-

body, in addition to the purchase of high per-obotics Laboratory at Yamagata University fol-formance motors and batteries, there was notlows a modular concept, being composed ofa signiﬁcant price reduction in comparison: inthree parts: a cart module responsible for mov-fact, it could amount to a higher value thaning the robot in the environment, a arm-handthe original electrical cart price. Considering

module responsible for collecting the cherries

both reasons together, the option was made towith a high precision manipulator, and a recog-perform the conversion.

nition system module responsible for identify-2.1

ing the environment (Fig.1).
In this paper, we present the conversion of acommercially available electrical cart designed
for carrying parcels into a fully controllable
robotic platform, focusing on building a wireless and modular system.

Body

The cart’s body was stripped, preserving only
the base metal frame and both front and backwheels. The parcel carrying frame was replacedwith a metal box of 64.5 × 47.5 × 26.5 cm insize, where the frame is made from aluminumbars and steel side panels, attached directly

2.

Cart Conversion

on top of the original metal frame. The orig-

This mobile platform is based on the Jasper
series

EJ20

electricalutility

cartfrom

CanyCom Company. The original cart con-inal driving motor was preserved, as well as
the 24 V battery pack. The steering handlebar
was replaced with a Maxon Combination number 313518 (Maxon Motor 268214 and Encoder–2–

tor direction and a Pulse-Width Modulation(PWM) analog signal to determine motor speed
for each motor, and directly converts it to an
equivalent power output to the motors basedon the available battery power connected (inthis case, the 24V battery pack).
Between the motor controller and the motors, there are two diﬀerent conﬁgurations ofMotor control system circuit board.

motor protection circuit. As recommended bythe manufacturer, a resettable PPTC 7) (Poly-Sensor 225785), added to a reduction gearheadmeric Positive Temperature Coeﬃcient device)of 1/100 ratio.

fuse of 4 A was added to the connector of theFig. 2

steering motor to prevent overcurrent damage.

3.

As this motor is dedicated to steering, such aElectronics

simple protection circuit is admissible.
A compact control circuit was designed basedThe transaxle motor had a built-in thermal

on the 24 V battery pack, the original 24 VDC

sensor and a solenoid brake from the manufac-

brushless driving transaxle motor and the re-turer, so these were considered in the design.

placed steering motor. All components were

The thermal sensor is a Normally Closed (NC)aﬃxed into a 15 × 20 cm square of acrylic plas-switch that opens in case of motor overtem-

tic, resulting in a control board of the same size

perature, triggered around 100◦ C. To integrate

and of 5 cm height (Fig. 2).

this, we added a Normally Open (NO) coil re-These dimensions facilitate the installation

lay switch between the motor controller andof the board inside the robot’s main body, mak-the motor connectors, activated by the thermal

ing it possible to ﬁxate it both horizontally andsensor power line: if the thermal sensor is trig-

vertically, and allowing for more space inside

gered, the connection opens, and the power tothe main body for other systems.

the motor is immediately cut-oﬀ. The solenoid

A Raspberry Pi 3 B board was chosen as thebreaker is a magnetic clamp that prevents themicrocontroller, due to its compatibility withmotor from turning. It can act as an emer-a wireless design (integrated Wi-Fi and blue-

gency brake, and also prevents the motor fromtooth antennas) and large input/output inter-turning when power is not applied or the cart

face (40 GPIO pins).

A Cytron MDDS10is parked. It is necessary to apply 24 V to it

SmartDrive board was chosen as the motor

to release the motor axis and allow for move-controller board, as it has the function of ament. As soon as power to the motor is cut,H-bridge controller capable of controlling two

this mechanical braker is activated, stopping

motors at the same time, as well as having

the cart.

overvoltage and overcurrent protection.

The steering motor (Maxon Combination)

The MDDS10 takes a high/low digital signal from the Raspberry Pi board to signal mo-has

abuilt-in

high-precision

quadrature

encoder, but the transaxle motor did not have–3–

Fig. 3External encoder implementation.

any sensing device.

The placement of themotor from the manufacturer made it diﬃcult
to implement an internal encoder, so we opted
for an external encoder device.
This has proved to be an issue more complicated than what we initially thought, due tothe limited space available in the main shaft.
Beside the space limitation, we also needed anequipment that would be resistant to humidity,
water drops, vibration, dust, etc. For this reason, we opted to use the MR080N Axial magnetic ring and LM10 incremental magnetic encoder sensor from RLS. To implement this sensor, we have designed support parts to facilitate the installation of both the magnetic ringand the encoder head: it consists of an accessory 3D printed ring that ﬁxates the magnetic
ring to the wheel coupling and of a 3D printed
support for the encoder head (Fig. 3).
To facilitate interfacing the encoders withthe microcontroller, a pulse counter Integrated
Circuit (IC) was added. The component chosen was the MikroE Counter Click, which is a
distribution board based on the 32-bit quadrature counter IC LS7366R. This IC is capableof counting the quadrature pulses from the en-Fig. 4
Electronics architecture for motorcontrol system.
through a joystick controller, a Playstation DualShock 4 Wireless Controller, connected tothe Raspberry board through bluetooth. Itis also possible to fully control the Raspberryboard wirelessly by conﬁguring it to connect
to a pocket Wi-Fi or network device beforehand. Although access through SSH and terminal commands is possible, the author uses
the NoMachine freeware software for thispurpose.
Fig. 4 shows a diagram of the described architecture.

coders up to an order of 32-bits and 40 MHz,and uses a Serial Peripheral Interface (SPI) for4.

communication.

Software

The Raspberry board has proven to be theFinally, the user input interface is done

correct choice for this project since it was easy–4–

to interface with all peripherals.
A few algorithms based on Python and ROS8)(Robot Operating System) were developed toperform the tasks of: receiving joystick input,
translating joystick input into a “desired speed”
command sent to the motors, sending speedcommands to the motors after PID adjustment,collecting encoder data and translating encoder
data into measured speed. Since we have botha “desired speed” input and “actual speed” in-Fig. 5 Cart prototype with in-development
world recognition system.

formation taken from the encoder sensors, themotor control is a closed feedback loop system

5.

that improves motor control accuracy.

World Recognition

Besides the basic motor control electronics

Mobile platform operation is fairly simple:

and software, the mobile platform prototype

ﬁrst, we connect a Notebook PC to the Rasp-

also includes an independent RTK GPS sensor

berry board through a Wi-Fi network using

and an in-development environment recogni-

the NoMachine software. Then the joysticktion and mapping system (Fig. 5).

is connected through Bluetooth to the Rasp-

The environment recognition and mapping

berry, and a script is started that constantly

system is composed of a Jetson Nano 2GB

listens to joystick inputs. When a joystick in-board and a D435 RealSense RGB-D camera.

put is received, that information is translatedVideo (image and depth) data is recorded byinto a desired motor speed quantity in meters

the Jetson board at 15 frames per second, andper second.

later it can be post-processed to evaluate

The desired speed information is then passedthrough a PID9) control calibrated for each

Visual Odometry and object recognition. Currently the project uses RTAB-Map SLAM10)

motor individually, and an adjusted command

software for mapping and Visual Odometry.

is sent to the motor controller board (MDDS10).

The RTK-GPS is an Emlid Reach M+ sys-

Meanwhile, another script initializes thetem, consisting of two Emlid Reach M+ boards

counter ICs and proceeds to listen to its read-and GPS antennas. This sensor has centimeterings. If there is a change in direction, overﬂow

precision when well calibrated and with goodor underﬂow of the counter, the IC signals asatellite vision (not cloudy, no overhead ob-

ﬂag event, which is heard by the script and astacles), as such, GPS data can be used to cor-ﬂag is signaled to adjust the readings accord-

rect odometry data, but is currently being usedingly. These counter readings are then trans-

as ground truth. The GPS rover module out-

lated to the current motor speed for each mo-puts its current location into a text ﬁle on thetor. Total travel speed is also available, based

Raspberry board through a Python script ev-

on the motor maximum speed and wheel size.

ery 200 ms.
This extra equipment continues to follow themodular design of the project, since it is in-–5–dependent from other systems, but it can beReferences

easily interfaced with through USB serial com-1) R. R. Shamshiri, C. Weltzien, I. A. Hameed,
I. J. Yule, T. E. Grift, S. K. Balasundram,
Lenka Pitonakova, D. Ahmad, G. Chowdhary: Research and development in agricultural robotics: A perspective of digital farming, Int. J. Agric. & Biol. Eng., 11-4, 1/14
(2018)

munication, I/O pins or even wirelessly.

6.

Conclusion and Future Work

The mobile platform developed consists ofa system with calibrated motor control, reliable encoder readings and accurate groundtruth GPS data information available for postprocessing. Since the program simply takes
a “desired speed” command for both steeringand driving, this architecture will be compatible with an autonomous driving software that
outputs the desired translation and rotation
speed. The further implementation of a visionsystem (Jetson board and RealSense camera)
evolved the prototype into the possibility of anautonomous drivable cart, with environmentand obstacle recognition. Hence, this mobile
platform achieves the necessary basic requirements of its initial proposal.
Highlights of the developed prototype are its
wireless and modular design, and easy to interface hardware and software.
Future work intends to expand to accurate
environment mapping and autonomous navigation in an agricultural scenario, and is currently being developed. The odometry data
and GPS data collected by the current software
can be used to improve mapping and odometry through many available open source SLAM
algorithms, including the initially proposed

2) J. Clapp, S. Ruder: Precision Technologies for Agriculture: Digital Farming, GeneEdited Crops, and the Politics of Sustainability, Global Environmental Politics, 20-3,
46/69 (2020)
3) MAFF,Statistics
ontheagriculturalworkforce(15Oct.
2021)
https://www.maff.go.jp/j/tokei/sihyo/data/08.html (in Japanese)4) M. A. Post, A. Bianco, X. T. Yan: Autonomous Navigation With Open Software
Platform for Field Robots, Proc. of Int. Conf.
on Informatics in Control, Automation andRobotics, Springer, Cham, 425/450 (2017)5) F. Shu, P. Lesur, Y. Xie, A. Pagani, D.
Stricker: SLAM in the Field: An Evaluation of Monocular Mapping and Localization on Challenging Dynamic Agricultural Environment, Proc. of the IEEE/CVF WinterConf. on Applications of Computer Vision,
1761/1771 (2021)
6) E. Obara, T. Seno, S. Bushizawa, N. Sasaki,S. Kanazawa, Y. Tsumaki, Cherry Harvesting Robot, Proc. of the 54th Annual Conf.
of JSME Tohoku Branch, 158/159 (2019) (inJapanese)
7) F. Doljack: PolySwitch PTC Devices - A
New Low-Resistance Conductive PolymerBased PTC Device for Overcurrent Protection, IEEE Trans. on Components, Hybrids,and Manufacturing Technology, 4-4, 372/378(1981)
8) H. Durrant-Whyte, T. Bailey: Simultaneous Localization and Mapping: Part I, IEEE
Robotics & Automation Magazine, 13-2,99/110 (2006)
9) K. H. Ang, G. Chong, Y. Li: PID Control System Analysis, Design, and Technology, IEEE
Trans. on Control Systems Technology 13-4,559/576 (2005)

RTAB-Map SLAM.

10) M. Labbe, F. Michaud, RTAB-Map as anOpen-Source Lidar and Visual SLAM Libraryfor Large-Scale and Long-Term Online Operation, Journal of Field Robotics, 36-2, 416/446(2019)–6–