Software Components of the Thorvald II Modular Robot

Grimstad, Lars; From, Pål Johan

doi:10.4173/mic.2018.3.2

Cited by 7 publications

(5 citation statements)

References 10 publications

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“…Simulation. Simulation of the robotic system and its operating environment can accelerate the development of robotic systems through quick and efficient tests and validation of the robot's design without physically building the robots (Figure 3) [46,54,102,103]. Simulation is also useful for developing and testing control algorithms, navigation algorithms, and data processing algorithms [60,94,103,104].…”

Section: Lidar-based Navigationmentioning

confidence: 99%

“…Gazebo is one of the most popular multirobot simulators which support a wide range of sensors and objects. It is open-source and is compatible with ROS and thus is used by many phenotyping robots for simulation [46,51,54,102,103]. However, Gazebo currently only supports Linux systems and lacks a good user interface.…”

Section: Lidar-based Navigationmentioning

confidence: 99%

“…Figure 3: Simulation of various phenotyping robots. (a) Thorvald II robots in Gazebo simulator[102]. (b) Simulation of an omnidirectional mobile robot in a typical vineyard in Gazebo simulator[103].…”

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confidence: 99%

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A Review of High-Throughput Field Phenotyping Systems: Focusing on Ground Robots

2022

Plant Phenomics

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Manual assessments of plant phenotypes in the field can be labor-intensive and inefficient. The high-throughput field phenotyping systems and in particular robotic systems play an important role to automate data collection and to measure novel and fine-scale phenotypic traits that were previously unattainable by humans. The main goal of this paper is to review the state-of-the-art of high-throughput field phenotyping systems with a focus on autonomous ground robotic systems. This paper first provides a brief review of nonautonomous ground phenotyping systems including tractors, manually pushed or motorized carts, gantries, and cable-driven systems. Then, a detailed review of autonomous ground phenotyping robots is provided with regard to the robot’s main components, including mobile platforms, sensors, manipulators, computing units, and software. It also reviews the navigation algorithms and simulation tools developed for phenotyping robots and the applications of phenotyping robots in measuring plant phenotypic traits and collecting phenotyping datasets. At the end of the review, this paper discusses current major challenges and future research directions.

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Section: Lidar-based Navigationmentioning

confidence: 99%

Section: Lidar-based Navigationmentioning

confidence: 99%

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A Review of High-Throughput Field Phenotyping Systems: Focusing on Ground Robots

2022

Plant Phenomics

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“…Two SCARA arms are mounted on Thorvald mobile robot [29] for fruit harvesting. Each arm is a 3-DoFs PRR 2 serial chain and consists of a cable-driven fingerslike end-effector whose role is to swallow a strawberry, center it, and finally cut it with an internal scissor.…”

Section: A Hardware Platformmentioning

confidence: 99%

Interactive Movement Primitives: Planning to Push Occluding Pieces for Fruit Picking

Mghames

Hanheide

Ghalamzan

2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Robotic technology is increasingly considered the major mean for fruit picking. However, picking fruits in a dense cluster imposes a challenging research question in terms of motion/path planning as conventional planning approaches may not find collision-free movements for the robot to reach-and-pick a ripe fruit within a dense cluster. In such cases, the robot needs to safely push unripe fruits to reach a ripe one. Nonetheless, existing approaches to planning pushing movements in cluttered environments either are computationally expensive or only deal with 2-D cases and are not suitable for fruit picking, where it needs to compute 3-D pushing movements in a short time. In this work, we present a path planning algorithm for pushing occluding fruits to reach-and-pick a ripe one. Our proposed approach, called Interactive Probabilistic Movement Primitives (I-ProMP), is not computationally expensive (its computation time is in the order of 100 milliseconds) and is readily used for 3-D problems. We demonstrate the efficiency of our approach with pushing unripe strawberries in a simulated polytunnel. Our experimental results confirm I-ProMP successfully pushes table top grown strawberries and reaches a ripe one.

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“…Full control strategies can be tested and developed with simulated sensors completing a feedback loop. Many agricultural robot drive systems and navigation techniques have been developed and tested in simulation prior to successful implementation [44][45][46] . A variety of simulation tools, such as V-REP, Gazebo, ArGOS and Webots, have been used and compared for robotics simulation in agriculture [40].…”

Section: Introductionmentioning

confidence: 99%

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

Iqbal

Sun

et al. 2020

Robotics

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The agriculture industry is in need of substantially increasing crop yield to meet growing global demand. Selective breeding programs can accelerate crop improvement but collecting phenotyping data is time- and labor-intensive because of the size of the research fields and the frequency of the work required. Automation could be a promising tool to address this phenotyping bottleneck. This paper presents a Robotic Operating System (ROS)-based mobile field robot that simultaneously navigates through occluded crop rows and performs various phenotyping tasks, such as measuring plant volume and canopy height using a 2D LiDAR in a nodding configuration. The efficacy of the proposed 2D LiDAR configuration for phenotyping is assessed in a high-fidelity simulated agricultural environment in the Gazebo simulator with an ROS-based control framework and compared with standard LiDAR configurations used in agriculture. Using the proposed nodding LiDAR configuration, a strategy for navigation through occluded crop rows is presented. The proposed LiDAR configuration achieved an estimation error of 6.6% and 4% for plot volume and canopy height, respectively, which was comparable to the commonly used LiDAR configurations. The hybrid strategy with GPS waypoint following and LiDAR-based navigation was used to navigate the robot through an agricultural crop field successfully with an root mean squared error of 0.0778 m which was 0.2% of the total traveled distance. The presented robot simulation framework in ROS and optimized LiDAR configuration helped to expedite the development of the agricultural robots, which ultimately will aid in overcoming the phenotyping bottleneck.

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Software Components of the Thorvald II Modular Robot

Cited by 7 publications

References 10 publications

A Review of High-Throughput Field Phenotyping Systems: Focusing on Ground Robots

A Review of High-Throughput Field Phenotyping Systems: Focusing on Ground Robots

Interactive Movement Primitives: Planning to Push Occluding Pieces for Fruit Picking

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

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