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( Tean Chen ),( Pablo Vela ),( Omer Faruk Ince ),( Woo-young Kim ),( Kyeong-hwan Lee ) 한국농업기계학회 2023 한국농업기계학회 학술발표논문집 Vol.28 No.2
The efficient navigation of autonomous mobile robots in agricultural greenhouses poses a critical challenge for precision farming and crop management. In this study, we present a navigation platform designed specifically for greenhouse environments. Our approach leverages Lidar-based Simultaneous Localization and Mapping (SLAM) to create an accurate map of the greenhouse while incorporating strategically placed markers to identify the center points of rails within the facility. During localization, we exploit the known positions of these rail markers in a 2D space, eliminating the need for additional markers and reducing computational complexity. The navigation phase relies on our robust localization algorithm, which matches Lidar scans with a 2D occupancy grid map to provide real-time position information for the robot. Path planning is accomplished using the Timed-Elastic-Band (TEB) local planner algorithm for dynamic obstacle avoidance and the Dijkstra global planner for route optimization. Experimental results demonstrate the effectiveness of our approach, with an average localization error of less than 5cm and our system achieves an impressive trajectory tracking accuracy rate exceeding 96%, enabling the autonomous robot to efficiently and safely navigate to specific target locations within the greenhouse. In future work, we will conduct experiments in more complex greenhouse environments to improve localization accuracy in agricultural robotics and autonomous systems.
Localization of a Rail Mobile Robot in Greenhouse using DeepLabCut Algorithm
( Tean Chen ),( Chulhyun Jo ),( Pablo Vela ),( Woo-young Kim ),( Kyeong-hwan Lee ) 한국농업기계학회 2023 한국농업기계학회 학술발표논문집 Vol.28 No.1
In recent years, the use of mobile robots in greenhouse environments has increased to improve the efficiency of plant cultivation and crop management. However, the precise localization of a rail mobile robot is essential for autonomous navigation in the greenhouse. In this study, we propose a novel approach using the DeepLabCut algorithm to localize rail mobile robots in greenhouse environments. A feedback control system, which includes a proportional-integralderivative (PID) controller and vision-based tracking, enables mobile robots to navigate specific path by recognizing the position of the rail and adjusting the velocity of the mobile robot and orientation to maintain the desired path. The algorithm was able to accurately localize the robot within the greenhouse environment, with an average localization error of less than 5 cm and an accuracy rate of over 92% in the trajectory tracking of the mobile robot. In future work, we will conduct experiments in more complex greenhouse environments.
상태 흐름 방법을 기반으로 실내 환경에서 외발 자전거 형 이동 로봇을 위한 경로 추종 플랫폼 개발
첸티안 ( Tean Chen ),유헌종 ( Heonjong Yoo ),이경환 ( Kyeonghwan Lee ) 한국농업기계학회 2020 한국농업기계학회 학술발표논문집 Vol.25 No.2
Localization is one of the important method for autonomous indoor robots to recognize it’s own position. In general, navigation of mobile robots is conducted using camera, Lider and GPS. But in case of indoor environment, GPS is unavailable. In this presentation, an autonomous indoor mobile robot, that is, a shuttle robot used a state flow method via ROS network: MATLAB and Linux high-level computers, IMU sensor, and then was able to obtain the cartesian coordinate information of the unicycle type mobile robot. After setting the pre-determined time based on the length of the path in the State Flow block, a path planning which is able to execute the work effectively is established using state flow algorithm. The state flow block produces time-series data sets which represents linear and angular velocities signals. Depending on the numerical values of the signal, the left and right motor rotational speed should be calculated through mobile robot forward kinematics. Several cases are considered: Case I) indicated the linear velocity is set positive certain value, and angular velocity is zero, so that the corresponding mobile robot moves forward. Case II) says that the linear velocity is set positive certain value and angular velocity is set positive certain value, which means the mobile robot turns right. Case III) says that the linear velocity is set positive certain value and angular velocity is set negative certain value, which means the corresponding mobile robot turns left. Case IV) says that the linear velocity is set negative certain value and angular velocity is set zero, which indicates the mobile robot moves backward. The effectiveness of the methods is demonstrated through desktop based developed indoor mobile robot’s control results.
첸티안 ( Tean Chen ),조철현 ( Jo Chulhyun ),파블러 ( Pablo Vela ),유헌종 ( Heonjong Yoo ),이경환 ( Kyeonghwan Lee ) 한국농업기계학회 2021 한국농업기계학회 학술발표논문집 Vol.26 No.2
In an agricultural indoor environment like a greenhouse, it is hard to utilize GPS sensor for estimating mobile robot position. So odometry sensor i.g. encoder is needed for indoor agricultural robot localization. In a structured agricultural indoor environment, simultaneous localization and mapping method using multi-sensors such as Lidar, IMU, encoder to find a destination can be inefficient. we propose that encoder-based localization method based on a pre-built indoor environment map for efficient navigation of a 2-wheel agricultural indoor mobile robot. First, we constructed a 2D indoor map using Lidar and IMU data, and in the constructed map, for effective localization of the mobile robot, we divided the free space that the indoor mobile robot can navigate and the obstacles that interfere with the robot's moving in advance. And then to estimate the robot localization we adopt odometer increment model using encoder measurements of the indoor mobile robot. In order to evaluate the localization accuracy performance of our proposed method, we compared with the localization performance of the existing SLAM algorithms- LOAM, LeGo LOAM, A LOAM- for the position accuracy and odometry measurement accuracy with comparison to the position of the pre-built map. The position accuracy and odometry prediction accuracy of our proposed algorithm were evaluated as the average of 96.0% and 97.2% respectively. In the future, we will conduct further experiments in real agricultural indoor environments such as greenhouses.
첸티안 ( Tean Chen ),조철현 ( Jo Chulhyun ),유헌종 ( Heonjong Yoo ),이경환 ( Kyeonghwan Lee ) 한국농업기계학회 2021 한국농업기계학회 학술발표논문집 Vol.26 No.1
In this presentation, the state flow method is utilized for path following indoor environment. Here, the novel voice recognition part is incorporated into path tracking problem using TCPIP communication. The two path following scenario was designed, and TCPIP receive block receives ASCII code from Android program. If the ASCII value is bigger than threshold value we set in the TCPIP receive block, the block itself chooses path 1, otherwise the block chooses path 2 in the real-time implementation. In order to implement the voice recognition based platform control, the Android program, MATLAB/SIMULINK program, Linux system are connected through ROS (Robot operating system) node connection. To summarize that, nodes are composed of three parts: the Android program, Linux system, MATLAB/SIMULINK program.
첸티안 ( Tean Chen ),조철현 ( Jo Chulhyun ),파블러 ( Pablo Vela ),유헌종 ( Heonjong Yoo ),이경환 ( Kyeonghwan Lee ) 한국농업기계학회 2022 한국농업기계학회 학술발표논문집 Vol.27 No.1
Accurate shuttle vehicle localization in the indoor environment on a precise map enables the mobile robot to estimate its position and orientation while moving in the indoor environment more efficiently. Trajectory tracking control is one of the fundamental techniques influencing a mobile robot's autonomous driving performance. MATLAB and Linux are high-level computers to equip the Inertial Measurement Unit (IMU), Velodyne VLP-16 channels LiDAR, and Encoder Sensors with the mobile robot platform. The mobile robot controls using a Robot Operating System (ROS) enabled robot, setup parameters for the differential wheels, and visualizes sensor data in a ROS robot visualization tool. In this paper, our model presents a new path following a method that integrates the pure pursuit algorithm and the state flow algorithm using the ROS Simulink model. The path following algorithm that performs autonomous waypoint navigation and obstacle avoidance method successfully localized and tested indoor environment. The accuracy improvement is demonstrated through several experimental results.
Active Path Planning Algorithm for Autonomous Mobile Robot Moving in Indoor Environment
첸티안 ( Tean Chen ),조철현 ( Chulhyun Cho ),파블러 ( Pablo Vela ),이경환 ( Kyeong-hwan Lee ) 한국농업기계학회 2022 한국농업기계학회 학술발표논문집 Vol.27 No.2
Accurate localization of mobile robots in indoor environment based on a precise map enables the mobile robots to estimate its position and orientation while moving in the indoor environment more efficiently. The objective of this study was to improve the mobile robot's navigation accuracy using the Rapidly Exploring Random Trees (RRTs) algorithm and the Hybrid A-Star algorithm, which can generate path planning capability in high-dimensional space for mobile robot localization. Localization and navigation for mobile robots were estimated using an inertial measurement unit, a LiDAR, and encoder sensors. The Robot Operating System (ROS) enabled the robot, set parameters for the differential wheels, and visualized sensor data in the visualization tool. The accuracy of the path planning method showed more than 91% success rate in mobile robot trajectory tracking. In the future, we will conduct experiments in more complicated areas, such as greenhouses and farms.
( Tianyuan Guan ),( Chen Tean ),( Sangeon Oh ),( Kyeonghwan Lee ) 한국농업기계학회 2019 한국농업기계학회 학술발표논문집 Vol.24 No.2
Autonomous robot have great potential to deal with various kinds of fieldwork. In spite of wheeled robot can adapt most landforms, it has to face to stability and trafficability problems in tough terrains. In order to apply to complicate outdoor environments, we develop a caterpillar equipped robot system with simplified Dynamic Window Approach for agriculture application. The caterpillar equipped robot system composed by a localization system which is integration of RTK-GPS, IMU and IMU auto-calibrate device, a Robot Operating System(ROS) high-level controller and a base controller. The caterpillar mobile robot has features of high traction and high mobility. Since it has differential drive architecture, different kinds of navigation algorithm can be easily applied to it. Dynamic Windows Approach(DWA) is a local navigation algorithm, which can select optimized velocity for robot by estimating its current heading and distance to goal, and it also can generate smooth path for robot. As the results of experiments, our robot can cruise certain path accurately even in rough terrain, and it can also correct orientation by itself periodically, it can satisfy the demand of outdoor agriculture usage.