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Cheng B, He X, Li X, Zhang N, Song W, Wu H. Research on Positioning and Navigation System of Greenhouse Mobile Robot Based on Multi-Sensor Fusion. SENSORS (BASEL, SWITZERLAND) 2024; 24:4998. [PMID: 39124046 PMCID: PMC11314800 DOI: 10.3390/s24154998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 07/13/2024] [Accepted: 07/31/2024] [Indexed: 08/12/2024]
Abstract
The labor shortage and rising costs in the greenhouse industry have driven the development of automation, with the core of autonomous operations being positioning and navigation technology. However, precise positioning in complex greenhouse environments and narrow aisles poses challenges to localization technologies. This study proposes a multi-sensor fusion positioning and navigation robot based on ultra-wideband (UWB), an inertial measurement unit (IMU), odometry (ODOM), and a laser rangefinder (RF). The system introduces a confidence optimization algorithm based on weakening non-line-of-sight (NLOS) for UWB positioning, obtaining calibrated UWB positioning results, which are then used as a baseline to correct the positioning errors generated by the IMU and ODOM. The extended Kalman filter (EKF) algorithm is employed to fuse multi-sensor data. To validate the feasibility of the system, experiments were conducted in a Chinese solar greenhouse. The results show that the proposed NLOS confidence optimization algorithm significantly improves UWB positioning accuracy by 60.05%. At a speed of 0.1 m/s, the root mean square error (RMSE) for lateral deviation is 0.038 m and for course deviation is 4.030°. This study provides a new approach for greenhouse positioning and navigation technology, achieving precise positioning and navigation in complex commercial greenhouse environments and narrow aisles, thereby laying a foundation for the intelligent development of greenhouses.
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Affiliation(s)
- Bo Cheng
- College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, China; (B.C.)
| | - Xueying He
- College of Horticulture, Henan Agricultural University, Zhengzhou 450002, China
| | - Xiaoyue Li
- College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, China; (B.C.)
| | - Ning Zhang
- College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, China; (B.C.)
| | - Weitang Song
- College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, China; (B.C.)
| | - Huarui Wu
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
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Ranjan R, Shin D, Jung Y, Kim S, Yun JH, Kim CH, Lee S, Kye J. Comparative Analysis of Integrated Filtering Methods Using UWB Localization in Indoor Environment. SENSORS (BASEL, SWITZERLAND) 2024; 24:1052. [PMID: 38400212 PMCID: PMC10892184 DOI: 10.3390/s24041052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Revised: 01/30/2024] [Accepted: 01/31/2024] [Indexed: 02/25/2024]
Abstract
This research delves into advancing an ultra-wideband (UWB) localization system through the integration of filtering technologies (moving average (MVG), Kalman filter (KF), extended Kalman filter (EKF)) with a low-pass filter (LPF). We investigated new approaches to enhance the precision and reduce noise of the current filtering methods-MVG, KF, and EKF. Using a TurtleBot robotic platform with a camera, our research thoroughly examines the UWB system in various trajectory situations (square, circular, and free paths with 2 m, 2.2 m, and 5 m distances). Particularly in the square path trajectory with the lowest root mean square error (RMSE) values (40.22 mm on the X axis, and 78.71 mm on the Y axis), the extended Kalman filter with low-pass filter (EKF + LPF) shows notable accuracy. This filter stands out among the others. Furthermore, we find that integrated method using LPF outperforms MVG, KF, and EKF consistently, reducing the mean absolute error (MAE) to 3.39% for square paths, 4.21% for circular paths, and 6.16% for free paths. This study highlights the effectiveness of EKF + LPF for accurate indoor localization for UWB systems.
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Affiliation(s)
- Rahul Ranjan
- Department of Computer and Electronic Convergence, Intelligent Robot Research Institute, Sun Moon University, Asan 31460, Republic of Korea;
| | - Donggyu Shin
- Department of Computer Engineering, Intelligent Robot Research Institute, Sun Moon University, Asan 31460, Republic of Korea; (D.S.); (Y.J.)
| | - Yoonsik Jung
- Department of Computer Engineering, Intelligent Robot Research Institute, Sun Moon University, Asan 31460, Republic of Korea; (D.S.); (Y.J.)
| | - Sanghyun Kim
- Department of Mechanical Engineering, Kyung Hee University, Suwon 17104, Republic of Korea;
| | - Jong-Hwan Yun
- Mobility Materials-Parts-Equipment Centre, Kongju National University, Kongju 32588, Republic of Korea;
| | - Chang-Hyun Kim
- Department of AI Machinery, Korea Institute of Machinery and Materials, Daejeon 34103, Republic of Korea;
| | - Seungjae Lee
- Department of Computer Engineering, Intelligent Robot Research Institute, Sun Moon University, Asan 31460, Republic of Korea; (D.S.); (Y.J.)
| | - Joongeup Kye
- Department of Mechanical Engineering, Sun Moon University, Asan 31460, Republic of Korea;
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Tan H, Zhao X, Zhai C, Fu H, Chen L, Yang M. Design and experiments with a SLAM system for low-density canopy environments in greenhouses based on an improved Cartographer framework. FRONTIERS IN PLANT SCIENCE 2024; 15:1276799. [PMID: 38362453 PMCID: PMC10867628 DOI: 10.3389/fpls.2024.1276799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Accepted: 01/03/2024] [Indexed: 02/17/2024]
Abstract
To address the problem that the low-density canopy of greenhouse crops affects the robustness and accuracy of simultaneous localization and mapping (SLAM) algorithms, a greenhouse map construction method for agricultural robots based on multiline LiDAR was investigated. Based on the Cartographer framework, this paper proposes a map construction and localization method based on spatial downsampling. Taking suspended tomato plants planted in greenhouses as the research object, an adaptive filtering point cloud projection (AF-PCP) SLAM algorithm was designed. Using a wheel odometer, 16-line LiDAR point cloud data based on adaptive vertical projections were linearly interpolated to construct a map and perform high-precision pose estimation in a greenhouse with a low-density canopy environment. Experiments were carried out in canopy environments with leaf area densities (LADs) of 2.945-5.301 m2/m3. The results showed that the AF-PCP SLAM algorithm increased the average mapping area of the crop rows by 155.7% compared with that of the Cartographer algorithm. The mean error and coefficient of variation of the crop row length were 0.019 m and 0.217%, respectively, which were 77.9% and 87.5% lower than those of the Cartographer algorithm. The average maximum void length was 0.124 m, which was 72.8% lower than that of the Cartographer algorithm. The localization experiments were carried out at speeds of 0.2 m/s, 0.4 m/s, and 0.6 m/s. The average relative localization errors at these speeds were respectively 0.026 m, 0.029 m, and 0.046 m, and the standard deviation was less than 0.06 m. Compared with that of the track deduction algorithm, the average localization error was reduced by 79.9% with the proposed algorithm. The results show that our proposed framework can map and localize robots with precision even in low-density canopy environments in greenhouses, demonstrating the satisfactory capability of the proposed approach and highlighting its promising applications in the autonomous navigation of agricultural robots.
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Affiliation(s)
- Haoran Tan
- College of Engineering, China Agricultural University, Beijing, China
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Xueguan Zhao
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- Beijing PAIDE Science and Technology Development Co., Ltd, Beijing, China
| | - Changyuan Zhai
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Hao Fu
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Liping Chen
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Minli Yang
- College of Engineering, China Agricultural University, Beijing, China
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Lv B, Wang X, Zhang R. A task level fusion autonomous switching mechanism. PLoS One 2023; 18:e0287791. [PMID: 37956151 PMCID: PMC10642799 DOI: 10.1371/journal.pone.0287791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 06/14/2023] [Indexed: 11/15/2023] Open
Abstract
Positioning technology is an important component of environmental perception. It is also the basis for autonomous decision-making and motion control of firefighting robots. However, some issues such as positioning in indoor scenarios still remain inherent challenges. The positioning accuracy of the fire emergency reaction dispatching (FERD) system is far from adequate to support some applications for firefighting and rescue in indoor scenarios with multiple obstacles. To solve this problem, this paper proposes a fusion module based on the Blackboard architecture. This module aims to improve the positioning accuracy of a single sensor of the unmanned vehicles within the FERD system. To reduce the risk of autonomous decision-making of the unmanned vehicles, this module uses a comprehensive manner of multiple channels to complement or correct the positioning of the firefighting robots. Specifically, this module has been developed to fusion a variety of relevant processes for precise positioning. This process mainly includes six strategies. These strategies are the denoising, spatial alignment, confidence degree update, observation filtering, data fusion, and fusion decision. These strategies merge with the current scenarios-related parameter data, empirical data on sensor errors, and information to form a series of norms. This paper then proceeds to gain experience data with the confidence degree, error of different sensors, and timeliness of this module by training in an indoor scenario with multiple obstacles. This process is from data of multiple sensors (bottom-level) to control decisions knowledge-based (up-level). This process can obtain globally optimal positioning results. Finally, this paper evaluates the performance of this fusion module for the FERD system. The experimental results show that this fusion module can effectively improve positioning accuracy in an indoor scenario with multiple obstacles. Code is available at https://github.com/lvbingyu-zeze/gopath/tree/master.
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Affiliation(s)
- Bingyu Lv
- College of Computer Science and Technology, Jilin University, Changchun, Jilin, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering, Jilin University, Changchun, Jilin, China
| | - Xianchang Wang
- College of Computer Science and Technology, Jilin University, Changchun, Jilin, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering, Jilin University, Changchun, Jilin, China
- Chengdu Kestrel Artificial Intelligence Institute, Sichuan, China
| | - Rui Zhang
- College of Computer Science and Technology, Jilin University, Changchun, Jilin, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering, Jilin University, Changchun, Jilin, China
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Jia L, Wang Y, Ma L, He Z, Li Z, Cui Y. Integrated Positioning System of Kiwifruit Orchard Mobile Robot Based on UWB/LiDAR/ODOM. SENSORS (BASEL, SWITZERLAND) 2023; 23:7570. [PMID: 37688027 PMCID: PMC10490773 DOI: 10.3390/s23177570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 08/24/2023] [Accepted: 08/28/2023] [Indexed: 09/10/2023]
Abstract
To address the issue of low positioning accuracy of mobile robots in trellis kiwifruit orchards with weak signal environments, this study investigated an outdoor integrated positioning method based on ultra-wideband (UWB), light detection and ranging (LiDAR), and odometry (ODOM). Firstly, a dynamic error correction strategy using the Kalman filter (KF) was proposed to enhance the dynamic positioning accuracy of UWB. Secondly, the particle filter algorithm (PF) was employed to fuse UWB/ODOM/LiDAR measurements, resulting in an extended Kalman filter (EKF) measurement value. Meanwhile, the odometry value served as the predicted value in the EKF. Finally, the predicted and measured values were fused through the EKF to estimate the robot's pose. Simulation results demonstrated that the UWB/ODOM/LiDAR integrated positioning method achieved a mean lateral error of 0.076 m and a root mean square error (RMSE) of 0.098 m. Field tests revealed that compared to standalone UWB positioning, UWB-based KF positioning, and LiDAR/ODOM integrated positioning methods, the proposed approach improved the positioning accuracy by 64.8%, 13.8%, and 38.3%, respectively. Therefore, the proposed integrated positioning method exhibits promising positioning performance in trellis kiwifruit orchards with potential applicability to other orchard environments.
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Affiliation(s)
- Liangsheng Jia
- College of Mechanical and Electrical Engineering, Northwest A&F University, Xianyang 712100, China
| | - Yinchu Wang
- College of Mechanical and Electrical Engineering, Northwest A&F University, Xianyang 712100, China
| | - Li Ma
- College of Mechanical and Electrical Engineering, Northwest A&F University, Xianyang 712100, China
| | - Zhi He
- College of Mechanical and Electrical Engineering, Northwest A&F University, Xianyang 712100, China
| | - Zixu Li
- College of Mechanical and Electrical Engineering, Northwest A&F University, Xianyang 712100, China
| | - Yongjie Cui
- College of Mechanical and Electrical Engineering, Northwest A&F University, Xianyang 712100, China
- Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Xianyang 712100, China
- Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Xianyang 712100, China
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