Field Evaluation of Path-Planning Algorithms for Autonomous Mobile Robot in Smart Farms

Pak, Jeonghyeon; Kim, Jeongin; Park, Yonghyun; Son, Hyoung Il

doi:10.1109/access.2022.3181131

Cited by 42 publications

(19 citation statements)

References 70 publications

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“…Ada 3 metode yang dibandikan yaitu Djikstra, A*, RRT. Dari percobaan yang dilakukan metode A* memberikan penyelesaian terbaik [7]. Kim mengembangkan robot beroda dengan kemampuan navigasi secara otomatis untuk memonitor kondisi tanaman pada perkebunan jagung.…”

Section: Pendahuluanunclassified

Rancang bangun bobot kartesian tiga axis untuk penyiraman tanaman yang akurat dan efisien

Tamami

Hermawan²,

Hanafi³

et al. 2022

eltek

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Untuk menunjang lahan pertanian yang subur, diperlukan proses penyiraman agar kadar air dalam tanah tetap terjaga. Kegiatan penyiraman yang dilakukan secara manual membutuhkan banyak energi. Selain itu kadar air yang diberikan dengan penyiraman manual tidak dapat terukur secara akurat. Dalam makalah ini, kami mengusulkan penyiraman otomatis dengan robot kartesian tiga aksis untuk lahan dengan ukuran 3 meter x 1.5 meter dengan 171 titik tanam. Kontrol penyiraman berbasis fuzzy agar kadar air yang diberikan bisa akurat. Sebelum penyiraman, rata-rata kelembapan tanah pada lahan tersebut adalah 45.28% dengan nilai minimal 40%, nilai maksimal 50%. Target kelembapan tanah untuk setiap titik adalah 60%. Robot dapat menyiram seluruh titik tanam tanpa campur tangan manusia. Nilai kadar air rata-rata penyiraman adalah 62.10%, dengan nilai minimal 60%, nilai maksimal 65%. To support fertile agricultural land, a watering process is needed so that the water content in the soil is maintained. Watering activities carried out manually require a lot of energy. In addition, the water content given by manual watering cannot be measured accurately. In this paper, we propose automatic watering with a three-axis Cartesian robot for land with a size of 3 meters x 1.5 meters with 171 planting points. Fuzzy based watering control so that the water content given can be accurate. Before watering, the average soil moisture on the land was 45.28% with a minimum value of 40%, a maximum value of 50%. The target soil moisture for each point is 60%. The robot can water the entire planting point without human intervention. The average water content value of watering is 62.10%, with a minimum value of 60%, a maximum value of 65%. In addition, also compared with the application error with the fuzzy method with the on-off method, the fuzzy method is able to produce more accurate watering with an average error rate of 2.10%, while the on-off method has an average error of 5.32% against the soil moisture target. The fuzzy method is also more time efficient in watering, which is 7 seconds to 8 seconds, while the on-off method requires a watering time of 10 seconds to 15 seconds

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Section: Pendahuluanunclassified

Rancang bangun bobot kartesian tiga axis untuk penyiraman tanaman yang akurat dan efisien

Tamami

Hermawan²,

Hanafi³

et al. 2022

eltek

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“…In contrast to people, mobile robots are generally capable of enduring repetitive tasks [1] and offer a less expensive alternative in the labor markets [2]. Robotic labor has been employed in various fields, including in the farmland [3][4][5][6][7], hospitals for patient care [8,9], and the food service industry or restaurants [10,11]. Based on the aforementioned tasks, mobile robots are required to follow a designed trajectory.…”

Section: Introductionmentioning

confidence: 99%

An improved fuzzy inference strategy using reinforcement learning for trajectory-tracking of a mobile robot under a varying slip ratio

Zaman,

2024

Robotica

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In this study, a fuzzy reinforcement learning control (FRLC) is proposed to achieve trajectory tracking of a differential drive mobile robot (DDMR). The proposed FRLC approach designs fuzzy membership functions to fuzzify the relative position and heading between the current position and a prescribed trajectory. Instead of fuzzy inference rules, the relationship between the fuzzy inputs and actuator voltage outputs is built using a reinforcement learning (RL) agent. Herein, the deep deterministic policy gradient (DDPG) methodology consisted of actor and critic neural networks is employed in the RL agent. Simulations are conducted with considering varying slip ratio disturbances, different initial positions, and two different trajectories in the testing environment. In the meantime, a comparison with the classical DDPG model is presented. The results show that the proposed FRLC is capable of successfully tracking different trajectories under varying slip ratio disturbances as well as having performance superiority to the classical DDPG model. Moreover, experimental results validate that the proposed FRLC is also applicable to real mobile robots.

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“…The Internet of Things (IoT) has recently played a significant role in the agricultural industry by supporting farmers [14]. IoT is quite helpful in uplifting living standards by transforming conventional technology into smart systems [15]. The Internet of Things (IoT) concept was designed to change people's everyday lives via multiple forms of computing and easy deployment of applications [16].…”

Section: Introductionmentioning

confidence: 99%

Monitoring and Controlling System of Smart Mini Greenhouse Based on Internet of Things (IoT) for Spinach Plant (Amaranthus sp.)

Putri,

Lestari,

Ifmalinda

et al. 2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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Greenhouses, often composed of plastic or glass structures, play a fundamental role in optimizing the environmental conditions essential for successful plant cultivation, ultimately resulting in higher-quality crop yields. The study aims to develop a real-time monitoring and control system for a smart mini greenhouse utilizing the Internet of Things (IoT), with a specific focus on cultivating spinach. The monitoring system is constructed around an ESP32 microcontroller, complemented by a DHT22 sensor for accurate air temperature and humidity measurements, and an RTC DS3231 timer for scheduling tasks. The DHT22 sensor's set point values trigger the fan and misting system operations. The fan activates when the air temperature reaches 32°C, while the misting system turns on when humidity levels (RH) reach 55%. The ESP32 is the central processing unit, enabling internet connectivity through Wi-Fi for real-time data monitoring via the Blynk application. Sensor calibration confirms the system's precision, with regression analyses producing impressive R2 values of 0.989 and 0.952. The shading net control system operates four hours daily, from 11:00 to 15:00 WIB (Western Indonesian Time), optimizing the greenhouse environment. This well-executed system leads to the robust growth of spinach plants, reaching a height of 23.02 cm, sprouting nine leaves, and attaining a weight of 10 grams. The findings from this study highlight the efficiency and effectiveness of the smart mini greenhouse's monitoring and control system, offering promising applications in broader greenhouse management and crop cultivation practices.

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Field Evaluation of Path-Planning Algorithms for Autonomous Mobile Robot in Smart Farms

Cited by 42 publications

References 70 publications

Rancang bangun bobot kartesian tiga axis untuk penyiraman tanaman yang akurat dan efisien

Rancang bangun bobot kartesian tiga axis untuk penyiraman tanaman yang akurat dan efisien

An improved fuzzy inference strategy using reinforcement learning for trajectory-tracking of a mobile robot under a varying slip ratio

Monitoring and Controlling System of Smart Mini Greenhouse Based on Internet of Things (IoT) for Spinach Plant (Amaranthus sp.)

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