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Integrated Navigation and Its Applications in Autonomous Agricultural Machinery

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Navigation and Positioning".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 2418

Special Issue Editors

School of Agriculture Engineering, Jiangsu University, Zhenjiang 212013, China
Interests: satellite positioning and inertial base combined navigation; autonomous operation collaborative control of agricultural machinery; agricultural machinery operation control and embedded system

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Guest Editor
School of Agriculture Engineering, Jiangsu University, Zhenjiang 212013, China
Interests: satellite navigation and inertial base integrated navigation; online monitoring of agricultural machinery equipment operation status; intelligent control and embedded system for agricultural machinery equipment operation process
School of Agriculture Engineering, Jiangsu University, Zhenjiang 212013, China
Interests: intelligent perception; transfer alignment; unmanned agricultural machinery and agricultural machinery navigation in particular

Special Issue Information

Dear Colleagues,

This Special Issue focuses on Integrated Navigation and Its Applications in Autonomous Agricultural Machinery, aiming to highlight the latest advancements, methodologies, and applications in this domain. Central to these advancements is a multi-sensor fusion approach that harnesses the complementary strengths of GNSS (specifically RTK) for centimeter-level absolute positioning accuracy; MEMS-IMU for stable motion sensing in dynamic environments; visual navigation for rich environmental perception and feature matching; and radar for robust detection under adverse weather or lighting conditions. Thereby, this integrated solution effectively addresses critical challenges including continuous positioning error accumulation in unstructured terrains, instantaneous interference from dynamic obstacles, attitude solution drift during complex maneuvers, and localization failures in feature-deprived environments.

We invite contributions exploring algorithm optimization (e.g., adaptive Kalman filtering, deep learning-based error correction), hardware-software co-design, and real-world validation in autonomous tractors, harvesters, and drones. Topics of interest include high-precision localization and attitude estimation, dynamic obstacle avoidance and path tracking, and multi-source sensor fusion for unstructured agricultural environments. Through research articles, reviews, and case studies, this Special Issue aims to provide a platform for specialized research on the application of integrated navigation in autonomous driving of agricultural machinery and equipment.

Dr. Bingbo Cui
Dr. Yongyun Zhu
Dr. Zhen Ma
Guest Editors

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Keywords

  • integrated navigation
  • agricultural vehicle autonomy
  • high-precision localization
  • dynamic obstacle avoidance
  • multi-source sensor fusion
  • unstructured agricultural environments

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Published Papers (1 paper)

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Research

20 pages, 8109 KB  
Article
Development of an Orchard Inspection Robot: A ROS-Based LiDAR-SLAM System with Hybrid A*-DWA Navigation
by Jiwei Qu, Yanqiu Gu, Zhinuo Qiu, Kangquan Guo and Qingzhen Zhu
Sensors 2025, 25(21), 6662; https://doi.org/10.3390/s25216662 - 1 Nov 2025
Viewed by 996
Abstract
The application of orchard inspection robots has become increasingly widespread. How-ever, achieving autonomous navigation in unstructured environments continues to pre-sent significant challenges. This study investigates the Simultaneous Localization and Mapping (SLAM) navigation system of an orchard inspection robot and evaluates its performance using [...] Read more.
The application of orchard inspection robots has become increasingly widespread. How-ever, achieving autonomous navigation in unstructured environments continues to pre-sent significant challenges. This study investigates the Simultaneous Localization and Mapping (SLAM) navigation system of an orchard inspection robot and evaluates its performance using Light Detection and Ranging (LiDAR) technology. A mobile robot that integrates tightly coupled multi-sensors is developed and implemented. The integration of LiDAR and Inertial Measurement Units (IMUs) enables the perception of environmental information. Moreover, the robot’s kinematic model is established, and coordinate transformations are performed based on the Unified Robotics Description Format (URDF). The URDF facilitates the visualization of robot features within the Robot Operating System (ROS). ROS navigation nodes are configured for path planning, where an improved A* algorithm, combined with the Dynamic Window Approach (DWA), is introduced to achieve efficient global and local path planning. The comparison of the simulation results with classical algorithms demonstrated the implemented algorithm exhibits superior search efficiency and smoothness. The robot’s navigation performance is rigorously tested, focusing on navigation accuracy and obstacle avoidance capability. Results demonstrated that, during temporary stops at waypoints, the robot exhibits an average lateral deviation of 0.163 m and a longitudinal deviation of 0.282 m from the target point. The average braking time and startup time of the robot at the four waypoints are 0.46 s and 0.64 s, respectively. In obstacle avoidance tests, optimal performance is observed with an expansion radius of 0.4 m across various obstacle sizes. The proposed combined method achieves efficient and stable global and local path planning, serving as a reference for future applications of mobile inspection robots in autonomous navigation. Full article
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