Mobile robotics in agricultural operations: A narrative review on planning aspects

The advent of mobile robots in agriculture has signaled a digital transformation with new automation technologies optimize a range of labor-intensive, resources-demanding, and time-consuming agri-field operations. To that end a generally accepted technical lexicon for mobile robots is lacking as pertinent terms are often used interchangeably. This creates confusion among research and practice stakeholders. In addition, a consistent definition of planning attributes in automated agricultural operations is still missing as relevant research is sparse. In this regard, a “narrative” review was adopted (1) to provide the basic terminology over technical aspects of mobile robots used in autonomous operations and (2) assess fundamental planning aspects of mobile robots in agricultural environments. Based on the synthesized evidence from extant studies, seven planning attributes have been included: (i) high-level control-specific attributes, which include reasoning architecture, the world model, and planning level, (ii) operation-specific attributes, which include locomotion–task connection and capacity constraints, and (iii) physical robot-specific attributes, which include vehicle configuration and vehicle kinematics.</jats:p

Procedures and Benefits of an Integrated Soil Mapping System for Directed Soil Sampling

There is an increasing need for the development of information systems that can optimize field management by leveraging the information obtained from the huge volume of data related to spatial variation within agricultural fields. To that end, an integrated soil mapping system for directed soil sampling is presented in this paper. The system architecture is analysed, highlighting the interactions between the individual subsystems toward capturing their internal structure. The final product constitutes a useful and effective tool for supporting field management as a result of in-depth study using state-of-the-art sensors, data fusion and decision-making algorithms. The benefits of using such a system are multifold including: (a) Optimization of the application of inputs on the farm; b) Reduction of the environmental footprint of agricultural practices; c) Increase of the economic benefit from the cultivation. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0

A Real-time Approach System for Vineyards Intra-row Weed Detection

With the incorporation of autonomous robotic platforms in various areas (industry, agriculture, etc.), numerous mundane operations have become fully automated. The highly demanding working environment of Agriculture let the development of techniques and machineries that could cope with each case. New technologies (from high performance motors to optimization algorithms) have been implemented and tested in this field. Every cultivation season, there are several operations that contribute to crop development and have to take place at least once. One of these operations is the weeding. In every crop, there are plants that are not part of it. These plants, in most cases have a negative impact on the crop and had to be removed. In the past the weeding was taken place either by hand (smaller fields) or by the use of herbicides (larger fields). In the second case, the dosage and the time are pre-defined, and they are not taking into consideration the growth percentage and the weed allocation within the field. In this work, a novel approach for intra-row weed detection in vineyards is developed and presented. All the experiments both for data collection and algorithm testing took place in a high value vineyard which produce numerous wine varieties. The aim of this work is to implement an accurate real-time robotic system for weed detection and segmentation using a deep learning algorithm in order to optimize the weeding procedure. This approach consists of two essential sub-systems. The first one is the robotic platform that embeds all the necessary sensors and the required computational power for the detection algorithm. The second one is the developed algorithm. From all the developed models, the selected one performed accurately in the training procedure and in the unknown datasets. In order to properly validate the algorithm, the unknown datasets were acquired in different time periods with variations in both camera angle and wine varieties. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0

UAV-Supported Route Planning for UGVs in Semi-Deterministic Agricultural Environments

Automated agricultural operations must be planned and organized to reduce risk and failure potential while optimizing productivity and efficiency. However, the diversity of natural outdoor environments and the varied data types and volumes required to represent an agricultural setting comprise critical challenges for the deployment of fully automated agricultural operations. In this regard, this study develops an integrated system for enabling an unmanned aerial vehicle (UAV) supported route planning system for unmanned ground vehicles (UGVs) in the semi-structured environment of orchards. The research focus is on the underpinning planning system components (i.e., world representation or map generation or perception and path planning). In particular, the system comprises a digital platform that receives as input a geotagged depiction of an orchard, which is obtained by a UAV. The pre-processed data define the agri-field's tracks that are transformed into a grid-based map capturing accessible areas. The grid map is then used to generate a topological path planning solution. Subsequently, the solution is translated into a sequence of coordinates that define the calculated optimal path for the UGV to traverse. The applicability of the developed system was validated in routing scenarios in a walnuts' orchard using a UGV. The contribution of the proposed system entails noise reduction techniques for the accurate representation of a semi-deterministic agricultural environment for enabling accuracy in the route planning of utilized automated machinery

An Integrated Real-Time Hand Gesture Recognition Framework for Human–Robot Interaction in Agriculture

Incorporating hand gesture recognition in human–robot interaction has the potential to provide a natural way of communication, thus contributing to a more fluid collaboration toward optimizing the efficiency of the application at hand and overcoming possible challenges. A very promising field of interest is agriculture, owing to its complex and dynamic environments. The aim of this study was twofold: (a) to develop a real-time skeleton-based recognition system for five hand gestures using a depth camera and machine learning, and (b) to enable a real-time human–robot interaction framework and test it in different scenarios. For this purpose, six machine learning classifiers were tested, while the Robot Operating System (ROS) software was utilized for “translating” the gestures into five commands to be executed by the robot. Furthermore, the developed system was successfully tested in outdoor experimental sessions that included either one or two persons. In the last case, the robot, based on the recognized gesture, could distinguish which of the two workers required help, follow the “locked” person, stop, return to a target location, or “unlock” them. For the sake of safety, the robot navigated with a preset socially accepted speed while keeping a safe distance in all interactions. © 2022 by the authors

UAV-Supported Route Planning for UGVs in Semi-Deterministic Agricultural Environments

Automated agricultural operations must be planned and organized to reduce risk and failure potential while optimizing productivity and efficiency. However, the diversity of natural outdoor environments and the varied data types and volumes required to represent an agricultural setting comprise critical challenges for the deployment of fully automated agricultural operations. In this regard, this study develops an integrated system for enabling an unmanned aerial vehicle (UAV) supported route planning system for unmanned ground vehicles (UGVs) in the semi-structured environment of orchards. The research focus is on the underpinning planning system components (i.e., world representation or map generation or perception and path planning). In particular, the system comprises a digital platform that receives as input a geotagged depiction of an orchard, which is obtained by a UAV. The pre-processed data define the agri-field’s tracks that are transformed into a grid-based map capturing accessible areas. The grid map is then used to generate a topological path planning solution. Subsequently, the solution is translated into a sequence of coordinates that define the calculated optimal path for the UGV to traverse. The applicability of the developed system was validated in routing scenarios in a walnuts’ orchard using a UGV. The contribution of the proposed system entails noise reduction techniques for the accurate representation of a semi-deterministic agricultural environment for enabling accuracy in the route planning of utilized automated machinery. © 2022 by the authors