Integrated Indoor Positioning System of Greenhouse Robot Based on UWB/IMU/ODOM/LIDAR.

UWB/IMU/ODOM/LIDAR greenhouse indoor positioning robots

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
25 Jun 2022
Historique:
received: 13 05 2022
revised: 17 06 2022
accepted: 23 06 2022
entrez: 9 7 2022
pubmed: 10 7 2022
medline: 10 7 2022
Statut: epublish

Résumé

Conventional mobile robots employ LIDAR for indoor global positioning and navigation, thus having strict requirements for the ground environment. Under the complicated ground conditions in the greenhouse, the accumulative error of odometer (ODOM) that arises from wheel slip is easy to occur during the long-time operation of the robot, which decreases the accuracy of robot positioning and mapping. To solve the above problem, an integrated positioning system based on UWB (ultra-wideband)/IMU (inertial measurement unit)/ODOM/LIDAR is proposed. First, UWB/IMU/ODOM is integrated by the Extended Kalman Filter (EKF) algorithm to obtain the estimated positioning information. Second, LIDAR is integrated with the established two-dimensional (2D) map by the Adaptive Monte Carlo Localization (AMCL) algorithm to achieve the global positioning of the robot. As indicated by the experiments, the integrated positioning system based on UWB/IMU/ODOM/LIDAR effectively reduced the positioning accumulative error of the robot in the greenhouse environment. At the three moving speeds, including 0.3 m/s, 0.5 m/s, and 0.7 m/s, the maximum lateral error is lower than 0.1 m, and the maximum lateral root mean square error (RMSE) reaches 0.04 m. For global positioning, the RMSEs of the x-axis direction, the y-axis direction, and the overall positioning are estimated as 0.092, 0.069, and 0.079 m, respectively, and the average positioning time of the system is obtained as 72.1 ms. This was sufficient for robot operation in greenhouse situations that need precise positioning and navigation.

Identifiants

pubmed: 35808314
pii: s22134819
doi: 10.3390/s22134819
pmc: PMC9269595
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Références

Sensors (Basel). 2017 Sep 10;17(9):
pubmed: 28891964
Sensors (Basel). 2019 May 14;19(10):
pubmed: 31091810
Sensors (Basel). 2020 Sep 14;20(18):
pubmed: 32937939
Sensors (Basel). 2022 Mar 16;22(6):
pubmed: 35336468

Auteurs

Zhenhuan Long (Z)

College of Mechanical and Electrical Engineering, Hunan Agriculture University, Changsha 410128, China.

Yang Xiang (Y)

College of Mechanical and Electrical Engineering, Hunan Agriculture University, Changsha 410128, China.

Xiangming Lei (X)

College of Mechanical and Electrical Engineering, Hunan Agriculture University, Changsha 410128, China.

Yajun Li (Y)

College of Mechanical and Electrical Engineering, Hunan Agriculture University, Changsha 410128, China.

Zhengfang Hu (Z)

College of Mechanical and Electrical Engineering, Hunan Agriculture University, Changsha 410128, China.

Xiufeng Dai (X)

College of Mechanical and Electrical Engineering, Hunan Agriculture University, Changsha 410128, China.

Classifications MeSH