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Techno-Economic and Environmental Analysis of an On-Grid and Off-Grid Renewable Energy Hybrid System in an Energy-Rich Rural Area: A Case in Indonesia Umam, Faikul; Wahyu, Fiki Milatul; Efendi, Mochamad Yusuf; Amir, Nizar; Gozan, Misri; Asmara, Yuli Panca
Journal of Robotics and Control (JRC) Vol 5, No 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22633

Developing a dedicated renewable energy hybrid system is a viable option for extending access to electrical energy in energy-rich rural areas. This study conducted a feasibility analysis of using a hybrid energy system, combining solar photovoltaic, wind, and biogas, to generate electricity and meet the energy needs of the rural area. West Waru Village is selected as the case study area for this research because it has abundant renewable energy sources. The Hybrid Optimization of Multiple Energy Resources (HOMER) tools is employed for modeling and optimizing the hybrid energy system, offering a comprehensive analysis encompassing technical, economic, and environmental aspects. Furthermore, the study's findings were further analyzed through a sensitivity analysis, considering unpredictable factors such as village load consumption, solar radiation, wind speed, and biomass availability. Additionally, the study’s results reveals that the renewable energy hybrid system can meet nearly 80% of the rural area's electrical energy requirements at a cost of $0.16 per kWh, resulting in the reduction of 8.4 million kg of carbon dioxide emissions. These findings can serve as a baseline for stakeholders in developing renewable energy systems in rural areas.

Towards Controlling Mobile Robot Using Upper Human Body Gesture Based on Convolutional Neural Network Fuad, Muhammad; Umam, Faikul; Wahyuni, Sri; Fahriani, Nuniek; Nurwahyudi, Ilham; Darwaman, Mochammad Ilham; Maulana, Fahmi
Journal of Robotics and Control (JRC) Vol 4, No 6 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i6.20399

Human-Robot Interaction (HRI) has challenges in investigation of a nonverbal and natural interaction. This study contributes to developing a gesture recognition system capable of recognizing the entire human upper body for HRI, which has never been done in previous research. Preprocessing is applied to improve image quality, reduce noise and highlight important features of each image, including color segmentation, thresholding and resizing. The hue, saturation, value (HSV) color segmentation is executed by utilizing blue color backdrop and additional lighting to deal with illumination issue. Then thresholding is performed to get a black and white image to distinguish between background and foreground. The resizing is completed to adjust the image to match the size expected by the model. The preprocessed data image is used as input for gesture recognition based on Convolutional Neural Network (CNN). This study recorded five gestures from five research subjects in difference gender and body posture with total of 450 images which divided into 380 and 70 images for training and testing respectively. Experiments that performed in an indoor environment showed that CNN achieved 92% of accuracy in the gesture recognition. It has lower level of accuracy compare to AlexNet model but with faster training computation time of 9 seconds. This result was obtained by testing the system over various distances. The optimal distance for a camera setting from user to interact with mobile robot by using gesture was 2.5 m. For future research, the proposed method will be improved and implemented for mobile robot motion control.

Implementation of Automatic DC Motor Braking PID Control System on (Disc Brakes) Budiarto, Hairil; Triwidyaningrum, Vivi; Umam, Faikul; Dafid, Ach
Journal of Robotics and Control (JRC) Vol 4, No 3 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i3.18505

The vital role of an automated braking system in ensuring the safety of motorized vehicles and their passengers cannot be overstated. It simplifies the braking process during driving, enhancing control and reducing the chances of accidents. This study is centered on the design of an automatic braking device for DC motors utilizing disc brakes. The instrument employed in this study was designed to accelerate the vehicle in two primary scenarios - before the collision with an obstacle and upon crossing the safety threshold. It achieves this by implementing the Proportional Integral Derivative (PID) control method. A significant part of this system comprises ultrasonic sensors, used for detecting the distance to obstructions, and rotary encoder sensors, which are utilized to measure the motor's rotational speed. These distance and speed readings serve as essential reference points for the braking process. The system is engineered to initiate braking when the distance value equals or falls below 60cm or when the speed surpasses 8000rpm. During such events, the disc brake is activated to reduce the motor's rotary motion. The suppression of the disc brake lever is executed pneumatically, informed by the sensor readings. Applying the PID method to the automatic braking system improved braking outcomes compared to a system without the PID method. This was proven by more effective braking results when the sensors detected specific distance and speed values. Numerous PID tuning tests achieved optimal results with K_p = 5, K_i = 1, and K_d = 3. These values can be integrated into automatic braking systems for improved performance. The PID method yielded more responsive braking outcomes when applied in distance testing. On the contrary, the braking results were largely unchanged in the absence of PID. Regarding speed testing, the PID method significantly improved the slowing down of the motor speed when it exceeded the maximum speed limit of 8000 rpm. This eliminates the possibility of sudden braking, thus maintaining the system within a safe threshold. The average time taken by the system to apply braking was 01.09 seconds, an indication of its quick responsiveness. This research is a valuable addition to control science, applying the PID control method to automatic DC motor braking. It provides valuable insights and concrete applications of PID control to complex mechatronic systems. It is also noteworthy for its development and optimization of suitable PID parameters to achieve responsive and stable braking. The study, therefore, offers a profound understanding of how PID control can be employed to manage braking systems on automatic DC motors, thereby advancing knowledge and application of control in control science and mechatronics.

Obstacle Avoidance Based on Stereo Vision Navigation System for Omni-directional Robot Umam, Faikul; Fuad, Muhammad; Suwarno, Iswanto; Ma'arif, Alfian; Caesarendra, Wahyu
Journal of Robotics and Control (JRC) Vol 4, No 2 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i2.17977

This paper addresses the problem of obstacle avoidance in mobile robot navigation systems. The navigation system is considered very important because the robot must be able to be controlled from its initial position to its destination without experiencing a collision. The robot must be able to avoid obstacles and arrive at its destination. Several previous studies have focused more on predetermined stationary obstacles. This has resulted in research results being difficult to apply in real environmental conditions, whereas in real conditions, obstacles can be stationary or moving caused by changes in the walking environment. The objective of this study is to address the robot’s navigation behaviors to avoid obstacles. In dealing with complex problems as previously described, a control system is designed using Neuro-Fuzzy so that the robot can avoid obstacles when the robot moves toward the destination. This paper uses ANFIS for obstacle avoidance control. The learning model used is offline learning. Mapping the input and output data is used in the initial step. Then the data is trained to produce a very small error. To support the movement of the robot so that it is more flexible and smoother in avoiding obstacles and can identify objects in real-time, a three wheels omnidirectional robot is used equipped with a stereo vision sensor. The contribution is to advance state of the art in obstacle avoidance for robot navigation systems by exploiting ANFIS with target-and-obstacles detection based on stereo vision sensors. This study tested the proposed control method by using 15 experiments with different obstacle setup positions. These scenarios were chosen to test the ability to avoid moving obstacles that may come from the front, the right, or the left of the robot. The robot moved to the left or right of the obstacles depending on the given Vy speed. After several tests with different obstacle positions, the robot managed to avoid the obstacle when the obstacle distance ranged from 173 – 150 cm with an average speed of Vy 274 mm/s. In the process of avoiding obstacles, the robot still calculates the direction in which the robot is facing the target until the target angle is 0.

Techno-Economic and Environmental Analysis of an On-Grid and Off-Grid Renewable Energy Hybrid System in an Energy-Rich Rural Area: A Case in Indonesia Umam, Faikul; Wahyu, Fiki Milatul; Efendi, Mochamad Yusuf; Amir, Nizar; Gozan, Misri; Asmara, Yuli Panca
Journal of Robotics and Control (JRC) Vol. 5 No. 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22633

Developing a dedicated renewable energy hybrid system is a viable option for extending access to electrical energy in energy-rich rural areas. This study conducted a feasibility analysis of using a hybrid energy system, combining solar photovoltaic, wind, and biogas, to generate electricity and meet the energy needs of the rural area. West Waru Village is selected as the case study area for this research because it has abundant renewable energy sources. The Hybrid Optimization of Multiple Energy Resources (HOMER) tools is employed for modeling and optimizing the hybrid energy system, offering a comprehensive analysis encompassing technical, economic, and environmental aspects. Furthermore, the study's findings were further analyzed through a sensitivity analysis, considering unpredictable factors such as village load consumption, solar radiation, wind speed, and biomass availability. Additionally, the study’s results reveals that the renewable energy hybrid system can meet nearly 80% of the rural area's electrical energy requirements at a cost of $0.16 per kWh, resulting in the reduction of 8.4 million kg of carbon dioxide emissions. These findings can serve as a baseline for stakeholders in developing renewable energy systems in rural areas.

Enhanced Precision Control of a 4-DOF Robotic Arm Using Numerical Code Recognition for Automated Object Handling Sukri, Hanifudin; Ibadillah, Achmad Fiqhi; Thinakaran, Rajermani; Umam, Faikul; Dafid, Ach.; Kurniawan, Adi; Morshed, Md. Monzur; Kurniawan, Denni
Journal of Robotics and Control (JRC) Vol. 6 No. 1 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

This research develops a 4-DOF robotic arm system that utilizes numerical codes for accurate, automated object handling, supporting advancements in sustainable industrial automation aligned with the UN Sustainable Development Goals (SDGs), particularly Industry, Innovation, and Infrastructure (SDG 9). Key contributions include the integration of EasyOCR for reliable code recognition and a control mechanism that enables precise positioning. The robotic system combines a webcam for visual sensing, servo motors for movement, and a gripper for object manipulation. EasyOCR effectively recognizes numerical codes on randomly positioned objects against a uniform background while the microcontroller calculates servo angles to guide the arm accurately to target positions. Testing results show a success rate exceeding 94% for detecting codes 1 to 4, with minor servo angle errors requiring adjustments in arm extension by 30 mm to 50 mm. Positional error analysis reveals an average error of less than 1.5 degrees. Although environmental factors like lighting can influence code visibility, this approach outperforms traditional methods in adaptability and precision. Future research will focus on enhancing code recognition under variable lighting and expanding the system's adaptability for diverse object types, broadening its applications in industries demanding high efficiency.

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