Simulation of an Autonomous Mobile Robot for LiDAR-Based In-Field Phenotyping and Navigation

Iqbal, Jawad; Xu, Rui; Sun, Shangpeng; Li, Changying

doi:10.3390/robotics9020046

Cited by 75 publications

(44 citation statements)

References 47 publications

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“…LIDAR, the acronym for ''Light Detection And Ranging'' is one kind of sensing method, which is often called a laser scanner or 2D LIDAR sensor as shown in Figure 7. LIDAR sensor is one of the most used sensors that measures distance with the help of illuminating a target by laser light and also measure the reflected light with a sensing element [66].…”

Section: ) Lidar Sensormentioning

confidence: 99%

Critical Design and Control Issues of Indoor Autonomous Mobile Robots: A Review

et al. 2021

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Robots that can move autonomously and can make intelligent decisions by perceiving their environments and surrounding objects are known as autonomous mobile robots. Such robots have rapidly moved from laboratories to automated industries to fill a variety of roles in our lives, homes, offices, hospitals, industries, and even on the streets. The interest in mobile robots is growing rapidly, prompting an enormous amount of research over the last 30 years, on critical factors of mobile robots such as locomotion, perception, localization, mapping, ego-motion tracking, and dynamic navigation. This article surveys these essential factors of autonomous mobile robots in terms of mathematical modeling, control issues, and challenging factors. Brief discussions are provided on the fundamentals of these technologies, popular algorithms in comprehensive mode, future challenges, and promising directions to guide the construction of an autonomous mobile robot with high accuracy and effectiveness. Since it is difficult to find complete coverage of those topics in a single location, this article provides a guideline for researchers entering the field or for innovators in the mobile robotics sector. The paper also examines open challenges in indoor mobile robots and identifies potential futures for autonomous mobile robots.

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Section: ) Lidar Sensormentioning

confidence: 99%

Critical Design and Control Issues of Indoor Autonomous Mobile Robots: A Review

et al. 2021

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“…For prototyping and testing of the ROS system a simulation twin was created of MARIA using Gazebo ( Figure 6A), a physics simulator, and ROS as the data communication interface. This simulation adds to our previous work with the addition of a three DoF manipulator [4]. A Universal Robot Description File (URDF) was generated from Solidworks using an URDF conversion plugin, and then revolute joints were added for the wheels and the manipulator joints.…”

Section: Ros Framework and Simulationmentioning

confidence: 99%

“…To address this phenotyping bottleneck, autonomous field robots have been investigated to perform phenotypic data collection iteratively throughput the growing season, not only to automate the data collection, but also to increase the temporal resolution of the data, making genetic analyses and breeding programs more effective [4]. A few notable autonomous mobile platforms have been developed such as the "Robotanist", a ground-based robot that can autonomously navigate sorghum and corn crop rows based on the Global Navigation Satellite Systems (GNSS) as well as deploy phenotyping sensors such as LiDAR and cameras to gather sub-canopy data [5].…”

Section: Introductionmentioning

confidence: 99%

“…With a 2D LiDAR, point clouds can be generated to determine important phenotypic traits of plants, such as canopy height and plant volume [25]. LiDAR has been used extensively on robotic mobile platforms for high-throughput phenotyping by statically mounting the 2D LiDAR on a mobile platform and moving it directly overhead or to the side of the plant [4,23,[25][26][27][28]. A low cost method of generating a 3D point cloud with a 2D LiDAR is to mount the LiDAR on a servo motor such that it "nods" back and forth to generate a 3D point cloud [29].…”

Section: Introductionmentioning

confidence: 99%

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Development of a Multi-Purpose Autonomous Differential Drive Mobile Robot for Plant Phenotyping and Soil Sensing

Iqbal

Halloran

et al. 2020

Electronics

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To help address the global growing demand for food and fiber, selective breeding programs aim to cultivate crops with higher yields and more resistance to stress. Measuring phenotypic traits needed for breeding programs is usually done manually and is labor-intensive, subjective, and lacks adequate temporal resolution. This paper presents a Multipurpose Autonomous Robot of Intelligent Agriculture (MARIA), an open source differential drive robot that is able to navigate autonomously indoors and outdoors while conducting plant morphological trait phenotyping and soil sensing. For the design of the rover, a drive system was developed using the Robot Operating System (ROS), which allows for autonomous navigation using Global Navigation Satellite Systems (GNSS). For phenotyping, the robot was fitted with an actuated LiDAR unit and a depth camera that can estimate morphological traits of plants such as volume and height. A three degree-of-freedom manipulator mounted on the mobile platform was designed using Dynamixel servos that can perform soil sensing and sampling using off-the-shelf and 3D printed components. MARIA was able to navigate both indoors and outdoors with an RMSE of 0.0156 m and 0.2692 m, respectively. Additionally, the onboard actuated LiDAR sensor was able to estimate plant volume and height with an average error of 1.76% and 3.2%, respectively. The manipulator performance tests on soil sensing was also satisfactory. This paper presents a design for a differential drive mobile robot built from off-the-shelf components that makes it replicable and available for implementation by other researchers. The validation of this system suggests that it may be a valuable solution to address the phenotyping bottleneck by providing a system capable of navigating through crop rows or a greenhouse while conducting phenotyping and soil measurements.

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“…A robot equipped with a Light detection and ranging system (LIDAR) and vision sensors was proposed for monitoring orchards [6,7]. Mahmud et al [8] presented a path planning approach for pesticide spraying in greenhouses, and Iqbal et al [9] proposed a simulation of a robotic platform based on 2D LIDAR for navigation and phenotyping tasks, like measuring canopy height. Recently, a literature review under the subject of agricultural robotics concluded that robotic systems were most explored for harvesting and weeding.…”

Section: Introductionmentioning

confidence: 99%

Occupancy Grid and Topological Maps Extraction from Satellite Images for Path Planning in Agricultural Robots

et al. 2020

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Robotics will significantly impact large sectors of the economy with relatively low productivity, such as Agri-Food production. Deploying agricultural robots on the farm is still a challenging task. When it comes to localising the robot, there is a need for a preliminary map, which is obtained from a first robot visit to the farm. Mapping is a semi-autonomous task that requires a human operator to drive the robot throughout the environment using a control pad. Visual and geometric features are used by Simultaneous Localisation and Mapping (SLAM) Algorithms to model and recognise places, and track the robot’s motion. In agricultural fields, this represents a time-consuming operation. This work proposes a novel solution—called AgRoBPP-bridge—to autonomously extract Occupancy Grid and Topological maps from satellites images. These preliminary maps are used by the robot in its first visit, reducing the need of human intervention and making the path planning algorithms more efficient. AgRoBPP-bridge consists of two stages: vineyards row detection and topological map extraction. For vineyards row detection, we explored two approaches, one that is based on conventional machine learning technique, by considering Support Vector Machine with Local Binary Pattern-based features, and another one found in deep learning techniques (ResNET and DenseNET). From the vineyards row detection, we extracted an occupation grid map and, by considering advanced image processing techniques and Voronoi diagrams concept, we obtained a topological map. Our results demonstrated an overall accuracy higher than 85% for detecting vineyards and free paths for robot navigation. The Support Vector Machine (SVM)-based approach demonstrated the best performance in terms of precision and computational resources consumption. AgRoBPP-bridge shows to be a relevant contribution to simplify the deployment of robots in agriculture.

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Simulation of an Autonomous Mobile Robot for LiDAR-Based In-Field Phenotyping and Navigation

Cited by 75 publications

References 47 publications

Critical Design and Control Issues of Indoor Autonomous Mobile Robots: A Review

Critical Design and Control Issues of Indoor Autonomous Mobile Robots: A Review

Development of a Multi-Purpose Autonomous Differential Drive Mobile Robot for Plant Phenotyping and Soil Sensing

Occupancy Grid and Topological Maps Extraction from Satellite Images for Path Planning in Agricultural Robots

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