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Electronics
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Modeling, Control, and Applications of Field Robotics
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Modeling, Control, and Applications of Field Robotics

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: closed (30 November 2020) | Viewed by 31743

Special Issue Editors

Prof. Dr. Hyoung Il Son

E-Mail Website
Guest Editor
1. Department of Convergence Biosystems Engineering, Chonnam National University, 88 Yongbong-ro, Buk-gu, Gwangju 81186, Korea
2. Department of Robotics Engineering Convergence, Chonnam National University, 88 Yongbong-ro, Buk-gu, Gwangju 81186, Korea
Interests: field robotics; hybrid systems; systems and synthetic biology; agricultural robotics
Prof. Dr. Myun Joong Hwang

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Korea National University of Transportation, 50 Daehak-ro, Chungju-si 27469, Chungbuk, Korea
Interests: robot motion and control; field robotics; robotic manipulation; autonomous systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Field robotics is concerned with the automation of vehicles and platforms to assist and/or replace humans performing tasks that are difficult, repetitive, unpleasant, or operate in harsh, unstructured environments. Field robotics encompasses the automation of many land, sea, and air platforms in applications such as agriculture, construction, mining, forestry, unban, underwater, military, and space. Field robotics is characterized by the application of the most advanced robotics principles in sensing, perception, control, and reasoning in unstructured and unknown environments. The appeal of field robotics is that it is challenging science, involves the latest engineering and systems design principles, and offers the real prospect of robotic principles making a substantial economic and social contribution to many different application areas. Recently, multi-robot systems has also become one of the main topics in the field to cover large-scale outdoor environments.

This Special Issue focuses on design, modeling, and control techniques for field robotics and possible applications of those results to give a large and complete view of complex research issues. We plan to invite a series of research results that span theoretical, design, and applied topics such as building robust field robots, (heterogeneous) multi-robot teams, environmental monitoring, and active robotic sensing and sampling.

Submissions to this Special Issue on ‘Modeling, Control, and Applications of Field Robotics’’ are solicited to represent a snapshot of the field’s development by covering a range of topics that include but are not limited to new methods, algorithms, solutions, and applications in the following areas:

  • Design of robotic systems for challenging field applications
  • Novel perceptions of field robots including passive and active methods
  • Mobile manipulators for active sensing and sampling
  • Long-term autonomy and navigation in unstructured environments
  • Data analytics and real-time decision making
  • Low-cost sensing and algorithms for full-day operations
  • Human user interfaces
  • Multi-robot coordination
  • Sensor networks
  • Large-scale mapping
  • Field robotics applications: agriculture, construction, mining, forestry, urban, underwater, military, space, and etc.

Prof. Dr. Hyoung Il Son
Prof. Dr. Myun Joong Hwang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Mobile robotics
  • Aerial robotics
  • Localization and mapping
  • Perception
  • Planning
  • Coordination
  • Control
  • Sensing
  • Monitoring
  • Sampling
  • Exploration
  • Surveillance and rescue
  • Agricultural robotics
  • Construction robotics
  • Underwater robotics
  • Marine robotics
  • Space robotics
  • Military robotics
  • Multi-robot systems
  • Heterogeneous robotics

Published Papers (6 papers)

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Research

15 pages, 2832 KiB  
Article
A Multiplicatively Weighted Voronoi-Based Workspace Partition for Heterogeneous Seeding Robots
by Jeongeun Kim, Chanyoung Ju and Hyoung Il Son
Electronics 2020, 9(11), 1813; https://doi.org/10.3390/electronics9111813 - 02 Nov 2020
Cited by 9 | Viewed by 2390
Abstract
Multi-robot systems (MRSs) are currently being used to perform agricultural tasks. In this regard, the deployment of heterogeneous MRSs will be essential for achieving more efficient and innovative farming in the future. In this paper, we propose a multiplicatively weighted (MW) Voronoi-based task-allocation [...] Read more.
Multi-robot systems (MRSs) are currently being used to perform agricultural tasks. In this regard, the deployment of heterogeneous MRSs will be essential for achieving more efficient and innovative farming in the future. In this paper, we propose a multiplicatively weighted (MW) Voronoi-based task-allocation scheme for heterogeneous agricultural robots. The seed points for area partitioning using a Voronoi diagram are obtained by performing node clustering using a k-means clustering algorithm. Heterogeneous robots have different specifications for performing various tasks. Thus, the proposed MW Voronoi-based area partitioning for heterogeneous robots is applied by considering various weighting factors. The path for each robot is computed such that the robot follows the nodes, and the computed paths serve as inputs for the workload distribution strategy that assigns paths to the robots. Simulations and field experiments were conducted to verify the effectiveness of the proposed approach. Full article
(This article belongs to the Special Issue Modeling, Control, and Applications of Field Robotics)
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19 pages, 23557 KiB  
Article
Development of a Multi-Purpose Autonomous Differential Drive Mobile Robot for Plant Phenotyping and Soil Sensing
by Jawad Iqbal, Rui Xu, Hunter Halloran and Changying Li
Electronics 2020, 9(9), 1550; https://doi.org/10.3390/electronics9091550 - 22 Sep 2020
Cited by 30 | Viewed by 10728
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Modeling, Control, and Applications of Field Robotics)
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20 pages, 10389 KiB  
Article
Task Space Trajectory Planning for Robot Manipulators to Follow 3-D Curved Contours
by Juhyun Kim, Maolin Jin, Sang Hyun Park, Seong Youb Chung and Myun Joong Hwang
Electronics 2020, 9(9), 1424; https://doi.org/10.3390/electronics9091424 - 02 Sep 2020
Cited by 8 | Viewed by 5744
Abstract
The demand for robots has increased in the industrial field, where robots are utilized in tasks that require them to move through complex paths. In the motion planning of a manipulator, path planning is carried out to determine a series of the positions [...] Read more.
The demand for robots has increased in the industrial field, where robots are utilized in tasks that require them to move through complex paths. In the motion planning of a manipulator, path planning is carried out to determine a series of the positions of robot end effectors without collision. Therefore, it is necessary to carry out trajectory planning to determine position, velocity, and acceleration over time and to control an actual industrial manipulator. Although several methods have already been introduced for point-to-point trajectory planning, a trajectory plan which moves through multiple knots is required to allow robots to adapt to more complicated tasks. In this study, a trajectory planning based on the Catmull–Rom spline is proposed to allow a robot to move via several points in a task space. A method is presented to assign intermediate velocities and time to satisfy the velocity conditions of initial and final knots. To optimize the motion of the robot, a time-scaling method is presented to minimize the margin between the physical maximum values of velocity and acceleration in real robots and the planned trajectory, respectively. A simulation is then performed to verify that the proposed method can plan the trajectory for moving multiple knots without stopping, and also to check the effects of control parameters. The results obtained show that the proposed methods are applicable to trajectory planning and require less computation compared with the cubic spline method. Furthermore, the robot follows the planned trajectory, and its motion does not exceed the maximum values of velocity and acceleration. An experiment is also executed to prove that the proposed method can be applied to real robotic tasks to dispense glue onto the sole in the shoe manufacturing process. The results from this experiment show that the robot can follow the 3-D curved contour in uniform speed using the proposed method. Full article
(This article belongs to the Special Issue Modeling, Control, and Applications of Field Robotics)
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21 pages, 8995 KiB  
Article
Center-Articulated Hydrostatic Cotton Harvesting Rover Using Visual-Servoing Control and a Finite State Machine
by Kadeghe Fue, Wesley Porter, Edward Barnes, Changying Li and Glen Rains
Electronics 2020, 9(8), 1226; https://doi.org/10.3390/electronics9081226 - 30 Jul 2020
Cited by 18 | Viewed by 4253
Abstract
Multiple small rovers can repeatedly pick cotton as bolls begin to open until the end of the season. Several of these rovers can move between rows of cotton, and when bolls are detected, use a manipulator to pick the bolls. To develop such [...] Read more.
Multiple small rovers can repeatedly pick cotton as bolls begin to open until the end of the season. Several of these rovers can move between rows of cotton, and when bolls are detected, use a manipulator to pick the bolls. To develop such a multi-agent cotton-harvesting system, each cotton-harvesting rover would need to accomplish three motions: the rover must move forward/backward, turn left/right, and the robotic manipulator must move to harvest cotton bolls. Controlling these actions can involve several complex states and transitions. However, using the robot operating system (ROS)-independent finite state machine (SMACH), adaptive and optimal control can be achieved. SMACH provides task level capability for deploying multiple tasks to the rover and manipulator. In this study, a center-articulated hydrostatic cotton-harvesting rover, using a stereo camera to locate end-effector and pick cotton bolls, was developed. The robot harvested the bolls by using a 2D manipulator that moves linearly horizontally and vertically perpendicular to the direction of the rover’s movement. We demonstrate preliminary results in an environment simulating direct sunlight, as well as in an actual cotton field. This study contributes to cotton engineering by presenting a robotic system that operates in the real field. The designed robot demonstrates that it is possible to use a Cartesian manipulator for the robotic harvesting of cotton; however, to reach commercial viability, the speed of harvest and successful removal of bolls (Action Success Ratio (ASR)) must be improved. Full article
(This article belongs to the Special Issue Modeling, Control, and Applications of Field Robotics)
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18 pages, 3770 KiB  
Article
A Multi-Objective Trajectory Planning Method for Collaborative Robot
by Jiangyu Lan, Yinggang Xie, Guangjun Liu and Manxin Cao
Electronics 2020, 9(5), 859; https://doi.org/10.3390/electronics9050859 - 22 May 2020
Cited by 17 | Viewed by 4148
Abstract
Aiming at the characteristics of high efficiency and smoothness in the motion process of collaborative robot, a multi-objective trajectory planning method is proposed. Firstly, the kinematics model of the collaborative robot is established, and the trajectory in the workspace is converted into joint [...] Read more.
Aiming at the characteristics of high efficiency and smoothness in the motion process of collaborative robot, a multi-objective trajectory planning method is proposed. Firstly, the kinematics model of the collaborative robot is established, and the trajectory in the workspace is converted into joint space trajectory using inverse kinematics method. Secondly, seven-order B-spline functions are used to construct joint trajectory sequences to ensure the continuous position, velocity, acceleration and jerk of each joint. Then, the trajectory competitive multi-objective particle swarm optimization (TCMOPSO) algorithm is proposed to search the Pareto optimal solutions set of the robot’s time-energy-jerk optimal trajectory. Further, the normalized weight function is proposed to select the appropriate solution. Finally, the algorithm simulation experiment is completed in MATLAB, and the robot control experiment is completed using the Robot Operating System (ROS). The experimental results show that the method can achieve effective multi-objective optimization, the appropriate optimal trajectory can be obtained according to the actual requirements, and the collaborative robot is actually operating well. Full article
(This article belongs to the Special Issue Modeling, Control, and Applications of Field Robotics)
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25 pages, 40852 KiB  
Article
A Novel FastSLAM Framework Based on 2D Lidar for Autonomous Mobile Robot
by Xu Lei, Bin Feng, Guiping Wang, Weiyu Liu and Yalin Yang
Electronics 2020, 9(4), 695; https://doi.org/10.3390/electronics9040695 - 24 Apr 2020
Cited by 8 | Viewed by 3724
Abstract
The autonomous navigation and environment exploration of mobile robots are carried out on the premise of the ability of environment sensing. Simultaneous localisation and mapping (SLAM) is the key algorithm in perceiving and mapping an environment in real time. FastSLAM has played an [...] Read more.
The autonomous navigation and environment exploration of mobile robots are carried out on the premise of the ability of environment sensing. Simultaneous localisation and mapping (SLAM) is the key algorithm in perceiving and mapping an environment in real time. FastSLAM has played an increasingly significant role in the SLAM problem. In order to enhance the performance of FastSLAM, a novel framework called IFastSLAM is proposed, based on particle swarm optimisation (PSO). In this framework, an adaptive resampling strategy is proposed that uses the genetic algorithm to increase the diversity of particles, and the principles of fractional differential theory and chaotic optimisation are combined into the algorithm to improve the conventional PSO approach. We observe that the fractional differential approach speeds up the iteration of the algorithm and chaotic optimisation prevents premature convergence. A new idea of a virtual particle is put forward as the global optimisation target for the improved PSO scheme. This approach is more accurate in terms of determining the optimisation target based on the geometric position of the particle, compared to an approach based on the maximum weight value of the particle. The proposed IFastSLAM method is compared with conventional FastSLAM, PSO-FastSLAM, and an adaptive generic FastSLAM algorithm (AGA-FastSLAM). The superiority of IFastSLAM is verified by simulations, experiments with a real-world dataset, and field experiments. Full article
(This article belongs to the Special Issue Modeling, Control, and Applications of Field Robotics)
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