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this figure show the three different states the robot can be in along with the battery and spray levels of each robot. The arrow shows the robot's current heading.
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Farmers are under growing pressure to increase production, a challenge that robotics has the potential to address. A possible solution is to replace large farm machinery with numerous smaller robots. However, with a large number of robots it will become increasingly time consuming for the farmer to monitor and control them all, hence the need for a...
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Citations
... Moreover, the travelling salesman problem methodology is applied on [174], [175] and [176] for the optimization of coverage planning for capacitated fields operations and route planning for sugarcane and avocado harvesting respectively. In [177], the field area coverage is handled by the users themselves through a UI that allows them to schedule the robots' routes taking also into account refilling stations to replenish their resources (e.g. pesticide and energy). ...
This paper presents a comprehensive review of ground agricultural robotic systems and applications with special focus on harvesting that span research and commercial products and results, as well as their enabling technologies. The majority of literature concerns the development of crop detection, field navigation via vision and their related challenges. Health monitoring, yield estimation, water status inspection, seed planting and weed removal are frequently encountered tasks. Regarding robotic harvesting, apples, strawberries, tomatoes and sweet peppers are mainly the crops considered in publications, research projects and commercial products. The reported harvesting agricultural robotic solutions, typically consist of a mobile platform, a single robotic arm/manipulator and various navigation/vision systems. This paper reviews reported development of specific functionalities and hardware, typically required by an operating agricultural robot harvester; they include (a) vision systems, (b) motion planning/navigation methodologies (for the robotic platform and/or arm), (c) Human-Robot-Interaction (HRI) strategies with 3D visualization, (d) system operation planning & grasping strategies and (e) robotic end-effector/gripper design. Clearly, automated agriculture and specifically autonomous harvesting via robotic systems is a research area that remains wide open, offering several challenges where new contributions can be made.
... Area coverage is a crucial factor for robotics application domains such as exploration [1], cleaning [2], painting [3], lawn mowing [4], and agriculture [5]. Hence, complete area coverage is one of the essential capabilities of a robot intended for these sorts of coverage demanding applications. ...
... Although existing partitioning techniques have implicit notions of optimality such as path efficiency (e.g. total turning angles) [8], they do not jointly optimize multiple objectives, which is important for finding sub-regions in which a fleet of UAVs can efficiently operate. In addition, despite specialized CPP algorithms that can handle specific types of constraints [13], [14], existing techniques are not good at finding optimal paths which also satisfy more general and complex constraints that are crucial for crop dusting. On the other hand, grid-based methods, as the name suggests, discretize the task area into uniform grids. ...
There is a strong demand for covering a large area autonomously by multiple UAVs (Unmanned Aerial Vehicles) supported by a ground vehicle. Limited by UAVs' battery life and communication distance, complete coverage of large areas typically involves multiple take-offs and landings to recharge batteries, and the transportation of UAVs between operation areas by a ground vehicle. In this paper, we introduce a novel large-area-coverage planning framework which collectively optimizes the paths for aerial and ground vehicles. Our method first partitions a large area into sub-areas, each of which a given fleet of UAVs can cover without recharging batteries. UAV operation routes, or trails, are then generated for each sub-area. Next, the assignment of trials to different UAVs and the order in which UAVs visit their assigned trails are simultaneously optimized to minimize the total UAV flight distance. Finally, a ground vehicle transportation path which visits all sub-areas is found by solving an asymmetric traveling salesman problem (ATSP). Although finding the globally optimal trail assignment and transition paths can be formulated as a Mixed Integer Quadratic Program (MIQP), the MIQP is intractable even for small problems. We show that the solution time can be reduced to close-to-real-time levels by first finding a feasible solution using a Random Key Genetic Algorithm (RKGA), which is then locally optimized by solving a much smaller MIQP.
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Computer simulation is an effective means for the research of robot navigation algorithms. In order to implement real-time, three-dimensional, and visual navigation algorithm simulation, a method of algorithm simulation based on secondary development of Unity3D is proposed. With this method, a virtual robot prototype can be created quickly with the imported 3D robot model, virtual joints, and virtual sensors, and then the navigation simulation can be carried out using the virtual prototype with the algorithm script in the virtual environment. Firstly, the scripts of the virtual revolute joint, virtual LiDAR sensors, and terrain environment are written. Secondly, the A* algorithm is improved for navigation in unknown 3D space. Thirdly, taking the Mecanum wheel mobile robot as an example, the 3D robot model is imported into Unity3D, and the virtual joint, sensor, and navigation algorithm scripts are added to the model. Then, the navigation is simulated in static and dynamic environments using a virtual prototype. Finally, the navigation tests of the physical robot are carried out in the physical environment, and the test trajectory is compared with the simulation trajectory. The simulation and test results validate the algorithm simulation method based on the redevelopment of Unity3d, showing that it is feasible, efficient, and flexible.
... Each platform requires a software development kit and framework. Native applications can also employ a variety of networking techniques to communicate to any robot middleware [15]. ...
Introducing the supervision system architecture of an agricultural robotic system enable the improved performance overcoming the complexity that current autonomous robots face due to the dynamic and unstructured agriculture environment. This requires the design of a human-robot interface. When designing a user interface several principles must be considered aiming to improve the usability of the user interface. This paper describes the design of the GUI web application and the coverage planner suitable for controlling the UV-Robot for typical coverage style greenhouse mildew treatment operations. The cross-platform user interface allows the farmer to specify their farm including fields, roads and docking stations, as well as controlling the whole operation. The contribution of this paper is to specify the design guidelines and the development of a user interface for a human-agricultural robot, in the case of UV treatment. Along with identifying the supervision system architecture and the communication control development, connecting the farmer, the UV-Robot and the cloud server associated.
Auch in der Landwirtschaft beginnt sich zunehmend ein höherer Grad an Automatisierung durchzusetzen. Hier spielen speziell Roboter eine wichtige Rolle, da auf Äckern, Feldern und im Wald eine hohe Variabilität gegeben ist und entsprechend eine hohe Flexibilität bei den eingesetzten Systemen gefordert wird. Einen sehr hohen Stellenwert nimmt hier vor allem die Sensorik ein, da eine korrekte Erfassung der Umwelt sowie der zu bearbeitenden Bereiche Grundvoraussetzung für ein erfolgreiches System ist. In dieser Arbeit werden exemplarisch realisierte Sensorlösungen auf Basis bildgebender Systeme vorgestellt, die in der Agrarrobotik sowie in der Landtechnik zukünftig hohes Einsatzpotenzial haben werden. Ihre Leistungen im Einsatz werden beurteilt, evaluiert und auf dieser Basis Anforderungen und Herausforderungen für zukünftige Systeme in diesem Segment diskutiert.
Farmers are under growing pressure to intensify production to feed a growing population while managing environmental impact. Robotics has the potential to address these challenges by replacing large complex farm machinery with fleets of small autonomous robots. This article presents our research toward the goal of developing teams of autonomous robots that perform typical farm coverage operations. Making a large fleet of autonomous robots economical requires the use of inexpensive sensors, such as cameras for localization and obstacle avoidance. To this end, we describe a vision-based obstacle detection system that continually adapts to environmental and illumination variations and a visionassisted localization system that can guide a robot along crop rows with a complex appearance. Large fleets of robots will become time-consuming to monitor, control, and resupply. To reduce this burden, we describe a vision-based docking system for autonomously refilling liquid supplies and an interface for controlling multiple robots.
