Actuators and sensors for application in agricultural robots: A review

In recent years, with the rapid development of science and technology, agricultural robots have gradually begun to replace humans, to complete various agricultural operations, changing traditional agricultural production methods. Not only is the labor input reduced, but also the production efficiency can be improved, which invariably contributes to the development of smart agriculture. This paper reviews the core technologies used for agricultural robots in non-structural environments. In addition, we review the technological progress of drive systems, control strategies, end-effectors, robotic arms, environmental perception, and other related systems. This research shows that in a non-structured agricultural environment, using cameras and light detection and ranging (LiDAR), as well as ultrasonic and satellite navigation equipment, and by integrating sensing, transmission, control, and operation, different types of actuators can be innovatively designed and developed to drive the advance of agricultural robots, to meet the delicate and complex requirements of agricultural products as operational objects, such that better productivity and standardization of agriculture can be achieved. In summary, agricultural production is developing toward a data-driven, standardized, and unmanned approach, with smart agriculture supported by actuator-driven-based agricultural robots. This paper concludes with a summary of the main existing technologies and challenges in the development of actuators for applications in agricultural robots, and the outlook regarding the primary development directions of agricultural robots in the near future

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

Manipulating Complex Robot Behavior for Autonomous and Continuous Operations

Service robot control faces challenges of dynamic environment and complex behavior, which mainly include eye-hand coordination and continuous operations. However, current programming scheme lacks the ability of managing such tasks. In this chapter, we propose a methodology of software development paradigm for the continuous operation of the dual-arm picking robot. First, a dual-arm robot is built for picking with the purpose of selectively harvesting in plant factory. Second, a hierarchical control software is framed by means of “Sense Plan Act” (SPA) paradigm. Third, based on the previous design, programming concept, and the ROS system, the sub-node programming of visual module, motion module, eye-hand coordination module, and task planning module are implemented with a state machine-based architecture. The experimental results show that if total number of targets within the visual field is not more than three, the average picking time is less than 35 s. The fluency of concurrent task management shows the feasibility of manipulating complex robot behavior for autonomous and continuous operations with the finite state machine model and task level architecture

Development and Evaluation of a Tomato Fruit Suction Cutting Device

This paper introduces a structure and harvesting motion for the suction cutting device of a tomato harvesting robot, and reports harvesting experiments conducted in a tomato greenhouse. The suction cutting device comprises a suction part and a cutting part. The suction part separates the target fruit from a tomato cluster and the cutting part cuts the peduncle of the target fruit. A photoresistor in the cutting part assesses whether or not the target fruit is harvestable, and the cutting motion is performed only when the fruit is assessed as harvestable. The harvesting experiments were conducted in a tomato greenhouse to evaluate the suction cutting device. In this experiments, 50 tomato clusters were randomly selected as the harvesting objects, and there were 203 tomato fruits (including immature fruits). Out of the 203 tomato fruits, 114 tomato fruits were mature and within the robot workspace. Out of the 114 tomato fruits, 105 tomato fruits were recognized as target fruits by the harvesting robot. Out of these 105 tomato fruits, 65 tomato fruits were assessed as harvestable and 55 were successfully harvested (the harvesting success rate was 85%). Based on the results of the harvesting experiments, this study clarified the issues of the suction cutting device, classified the fruits according to whether they were easy or difficult to harvest, and evaluated the fruit characteristics qualitatively.2021 IEEE/SICE International Symposium on System Integrations (SII), January 11 - 14, 2021, Iwaki, Fukushima, Japa

Automatic Tomato and Peduncle Location System Based on Computer Vision for Use in Robotized Harvesting

Protected agriculture is a field in which the use of automatic systems is a key factor. In fact, the automatic harvesting of delicate fruit has not yet been perfected. This issue has received a great deal of attention over the last forty years, although no commercial harvesting robots are available at present, mainly due to the complexity and variability of the working environments. In this work we developed a computer vision system (CVS) to automate the detection and localization of fruit in a tomato crop in a typical Mediterranean greenhouse. The tasks to be performed by the system are: (1) the detection of the ripe tomatoes, (2) the location of the ripe tomatoes in the XY coordinates of the image, and (3) the location of the ripe tomatoes’ peduncles in the XY coordinates of the image. Tasks 1 and 2 were performed using a large set of digital image processing tools (enhancement, edge detection, segmentation, and the feature’s description of the tomatoes). Task 3 was carried out using basic trigonometry and numerical and geometrical descriptors. The results are very promising for beef and cluster tomatoes, with the system being able to classify 80.8% and 87.5%, respectively, of fruit with visible peduncles as “collectible”. The average processing time per image for visible ripe and harvested tomatoes was less than 30 ms

Biologically inspired robotic perception-action for soft fruit harvesting in vertical growing environments

Multiple interlinked factors like demographics, migration patterns, and economics are presently leading to the critical shortage of labour available for low-skilled, physically demanding tasks like soft fruit harvesting. This paper presents a biomimetic robotic solution covering the full ‘Perception-Action’ loop targeting harvesting of strawberries in a state-of-the-art vertical growing environment. The novelty emerges from both dealing with crop/environment variance as well as configuring the robot action system to deal with a range of runtime task constraints. Unlike the commonly used deep neural networks, the proposed perception system uses conditional Generative Adversarial Networks to identify the ripe fruit using synthetic data. The network can effectively train the synthetic data using the image-to-image translation concept, thereby avoiding the tedious work of collecting and labelling the real dataset. Once the harvest-ready fruit is localised using point cloud data generated by a stereo camera, our platform’s action system can coordinate the arm to reach/cut the stem using the Passive Motion Paradigm framework inspired by studies on neural control of movement in the brain. Results from field trials for strawberry detection, reaching/cutting the stem of the fruit, and extension to analysing complex canopy structures/bimanual coordination (searching/picking) are presented. While this article focuses on strawberry harvesting, ongoing research towards adaptation of the architecture to other crops such as tomatoes and sweet peppers is briefly described

Design and walking analysis of proposed four-legged glass cleaning robot

In this study, a legged and wheeled robot model was proposed for cleaning the glass of greenhouses. The robot has four wheels and four legs, each with three degrees of freedom (DOF). The design, kinematic analysis and simulation of the robot was carried out. Glass greenhouses are created by placing glass sheets on T-shaped iron bars arranged in parallel at certain intervals. The robot performs the glass cleaning task by performing two different movements on greenhouse roof. As a first movement, the robot moves like a train moving on the rail on iron bars with wheels, cleaning the glass as it travels. After cleaning the glasses placed between two iron bars along a column, as second movement, the robot passes the next column using legs. These two movements continue until the entire roof of the greenhouse is cleaned. Kinematic analysis of this robot, which is designed with mechanical properties that can make these movements, has been made. Walking simulation of the robot was carried out according to the kinematic analysis. The simulation results showed that this proposed robot can be used to clean glass on the greenhouse roof

ABC: Adaptive, Biomimetic, Configurable Robots for Smart Farms - From Cereal Phenotyping to Soft Fruit Harvesting

Currently, numerous factors, such as demographics, migration patterns, and economics, are leading to the critical labour shortage in low-skilled and physically demanding parts of agriculture. Thus, robotics can be developed for the agricultural sector to address these shortages. This study aims to develop an adaptive, biomimetic, and configurable modular robotics architecture that can be applied to multiple tasks (e.g., phenotyping, cutting, and picking), various crop varieties (e.g., wheat, strawberry, and tomato) and growing conditions. These robotic solutions cover the entire perception–action–decision-making loop targeting the phenotyping of cereals and harvesting fruits in a natural environment. The primary contributions of this thesis are as follows. a) A high-throughput method for imaging field-grown wheat in three dimensions, along with an accompanying unsupervised measuring method for obtaining individual wheat spike data are presented. The unsupervised method analyses the 3D point cloud of each trial plot, containing hundreds of wheat spikes, and calculates the average size of the wheat spike and total spike volume per plot. Experimental results reveal that the proposed algorithm can effectively identify spikes from wheat crops and individual spikes. b) Unlike cereal, soft fruit is typically harvested by manual selection and picking. To enable robotic harvesting, the initial perception system uses conditional generative adversarial networks to identify ripe fruits using synthetic data. To determine whether the strawberry is surrounded by obstacles, a cluster complexity-based perception system is further developed to classify the harvesting complexity of ripe strawberries. c) Once the harvest-ready fruit is localised using point cloud data generated by a stereo camera, the platform’s action system can coordinate the arm to reach/cut the stem using the passive motion paradigm framework, as inspired by studies on neural control of movement in the brain. Results from field trials for strawberry detection, reaching/cutting the stem of the fruit with a mean error of less than 3 mm, and extension to analysing complex canopy structures/bimanual coordination (searching/picking) are presented. Although this thesis focuses on strawberry harvesting, ongoing research is heading toward adapting the architecture to other crops. The agricultural food industry remains a labour-intensive sector with a low margin, and cost- and time-efficiency business model. The concepts presented herein can serve as a reference for future agricultural robots that are adaptive, biomimetic, and configurable

Current status and future trends in agricultural robotics

This paper analyzes some of the innovations in agricultural robotics, specifically for weed control, harvesting and monitoring, taking into account the challenges of introducing robotics in this sector, such as fruit detection, orchard navigation, task planning algorithms, or sensors optimization. One of the trends in agriculture 4.0 is the introduction of swarm robotics, allowing collaboration between robots. Another trend is in aerial imagery acquisition for ground analysis as well as environmental reconstruction, complemented by field-mounted sensors. Although robots are becoming quite important in the evolution of agriculture, it is still unlikely that all tasks will be automated in the near future due to the complexity arised by the overall variability of cultures.Este trabalho de investigação é financiado pelo projeto PrunusBot - Sistema robótico aéreo autónomo de pulverização controlada e previsão de produção frutícola, Operação n.º PDR2020- 101-031358 (líder), Consórcio n.º 340, Iniciativa n.º 140, promovido pelo PDR2020 e cofinanciado pelo FEADER e União Europeia no âmbito do Programa Portugal 2020.info:eu-repo/semantics/publishedVersio