5,880 research outputs found
Aspects of automation of selective cleaning
Cleaning (pre-commercial thinning) is a silvicultural operation, primarily used to improve growing conditions of remaining trees in young stands (ca. 3 - 5 m of height). Cleaning costs are considered high in Sweden and the work is laborious. Selective cleaning with autonomous artificial agents (robots) may rationalise the work, but requires new knowledge. This thesis aims to analyse key issues regarding automation of cleaning; suggesting general solutions and focusing on automatic selection of main-stems. The essential requests put on cleaning robots are to render acceptable results and to be cost competitive. They must be safe and be able to operate independently and unattended for several hours in a dynamic and non-deterministic environment. Machine vision, radar, and laser scanners are promising techniques for obstacle avoidance, tree identification, and tool control. Horizontal laser scannings were made, demonstrating the possibility to find stems and make estimations regarding their height and diameter. Knowledge regarding stem selections was retrieved through qualitative interviews with persons performing cleaning. They consider similar attributes of trees, and these findings and current cleaning manuals were used in combination with a field inventory in the development of a decision support system (DSS). The DSS selects stems by the attributes species, position, diameter, and damage. It was used to run computer-based simulations in a variety of young forests. A general follow-up showed that the DSS produced acceptable results. The DSS was further evaluated by comparing its selections with those made by experienced cleaners, and by a test in which laymen performed cleanings following the system. The DSS seems to be useful and flexible, since it can be adjusted in accordance with the cleanersâ results. The laymenâs results implied that the DSS is robust and that it could be used as a training tool. Using the DSS in automatic, or semi-automatic, cleaning operations should be possible if and when selected attributes can be automatically perceived. A suitable base-machine and thorough research, regarding e.g. safety, obstacle avoidance, and target identification, is needed to develop competitive robots. However, using the DSS as a training-tool for inexperienced cleaners could be an interesting option as of today
Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy
With the advent of agriculture 3.0 and 4.0, researchers are increasingly
focusing on the development of innovative smart farming and precision
agriculture technologies by introducing automation and robotics into the
agricultural processes. Autonomous agricultural field machines have been
gaining significant attention from farmers and industries to reduce costs,
human workload, and required resources. Nevertheless, achieving sufficient
autonomous navigation capabilities requires the simultaneous cooperation of
different processes; localization, mapping, and path planning are just some of
the steps that aim at providing to the machine the right set of skills to
operate in semi-structured and unstructured environments. In this context, this
study presents a low-cost local motion planner for autonomous navigation in
vineyards based only on an RGB-D camera, low range hardware, and a dual layer
control algorithm. The first algorithm exploits the disparity map and its depth
representation to generate a proportional control for the robotic platform.
Concurrently, a second back-up algorithm, based on representations learning and
resilient to illumination variations, can take control of the machine in case
of a momentaneous failure of the first block. Moreover, due to the double
nature of the system, after initial training of the deep learning model with an
initial dataset, the strict synergy between the two algorithms opens the
possibility of exploiting new automatically labeled data, coming from the
field, to extend the existing model knowledge. The machine learning algorithm
has been trained and tested, using transfer learning, with acquired images
during different field surveys in the North region of Italy and then optimized
for on-device inference with model pruning and quantization. Finally, the
overall system has been validated with a customized robot platform in the
relevant environment
Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms
The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent âdevicesâ, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew âcognitive devicesâ are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications
Towards Autonomous Selective Harvesting: A Review of Robot Perception, Robot Design, Motion Planning and Control
This paper provides an overview of the current state-of-the-art in selective
harvesting robots (SHRs) and their potential for addressing the challenges of
global food production. SHRs have the potential to increase productivity,
reduce labour costs, and minimise food waste by selectively harvesting only
ripe fruits and vegetables. The paper discusses the main components of SHRs,
including perception, grasping, cutting, motion planning, and control. It also
highlights the challenges in developing SHR technologies, particularly in the
areas of robot design, motion planning and control. The paper also discusses
the potential benefits of integrating AI and soft robots and data-driven
methods to enhance the performance and robustness of SHR systems. Finally, the
paper identifies several open research questions in the field and highlights
the need for further research and development efforts to advance SHR
technologies to meet the challenges of global food production. Overall, this
paper provides a starting point for researchers and practitioners interested in
developing SHRs and highlights the need for more research in this field.Comment: Preprint: to be appeared in Journal of Field Robotic
Automatic Romaine Heart Harvester
The Romaine Robotics Senior Design Team developed a romaine lettuce heart trimming system in partnership with a Salinas farm to address a growing labor shortage in the agricultural industry that is resulting in crops rotting in the field before they could be harvested. An automated trimmer can alleviate the most time consuming step in the cut-trim-bag harvesting process, increasing the yields of robotic cutters or the speed of existing laborer teams. Leveraging the Partner Farmâs existing trimmer architecture, which consists of a laborer loading lettuce into sprungloaded grippers that are rotated through vision and cutting systems by an indexer, the team redesigned geometry to improve the loading, gripping, and ejection stages of the system. Physical testing, hand calculations, and FEA were performed to understand acceptable grip strengths and cup design, and several wooden mockups were built to explore a new actuating linkage design for the indexer. The team manufactured, assembled, and performed verification testing on a full-size metal motorized prototype that can be incorporated with the Partner Farmâs existing cutting and vision systems. The prototype met all of the established requirements, and the farm has implemented the redesign onto their trimmer. Future work would include designing and implementing vision and cutting systems for the teamâs metal prototype
Robots in Agriculture: State of Art and Practical Experiences
The presence of robots in agriculture has grown significantly in recent years, overcoming some of the challenges and complications of this field. This chapter aims to collect a complete and recent state of the art about the application of robots in agriculture. The work addresses this topic from two perspectives. On the one hand, it involves the disciplines that lead the automation of agriculture, such as precision agriculture and greenhouse farming, and collects the proposals for automatizing tasks like planting and harvesting, environmental monitoring and crop inspection and treatment. On the other hand, it compiles and analyses the robots that are proposed to accomplish these tasks: e.g. manipulators, ground vehicles and aerial robots. Additionally, the chapter reports with more detail some practical experiences about the application of robot teams to crop inspection and treatment in outdoor agriculture, as well as to environmental monitoring in greenhouse farming
- âŠ