An algorithm defining the motions of a citrus picking robot (original) (raw)
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Near-minimum-time task planning for fruit-picking robots
IEEE Transactions on Robotics and Automation, 1991
A near-minimum-time task-planning algorithm for fruitharvesting robots having to pick fruits at N given locations is presented. For the given kinematic and inertial parameters of the manipulator, the algorithm determines the near-optimal sequence of fruit locations through which the arm should pass and finds the near-minimum-time path between these points. The sequence of motions was obtained by solving the Traveling Salesman Problem (TSP) using the distance along the geodesics in the manipulator's inertia space, between every two fruit locations, as the cost to be minimized. The algorithm presented here was applied to define the motions of a citrus-picking robot and was tested for a cylindrical robot on fruit position data collected from 20 trees. Significant reduction in the required computing time was achieved by dividing the volume containing the fruits into subvolumes and estimating the geodesic distance rather than calculating it. Nevertheless, in most cases the solution of the TSP, based on the estimated geodesic distance, produced nearly the same fruit sequence as the one resulting from the use of the exact geodesic distance between the fruit locations. Results of simulation tests enabled us to assess the influence of the robot's kinematic and dynamic parameters and of the spatial distribution of fruits on the motion sequence being selected. The proposed algorithm can help in selecting the most efficient robot design for any robot having to perform a sequence of tasks at N known locations.
IEEE Access, 2021
Modern commercial orchards are increasingly adopting trees of SNAP architectures (Simple, Narrow, Accessible, and Productive) as the fruits on such trees are, in general, more easily reachable by human or robotic harvesters. This article presents a methodology that utilizes three dimensional (3D) digitized computer models of high-density pear and cling-peach trees, and fruit positions to quantify the linear fruit reachability (LFR) of such trees, i.e., their reachability by telescoping robot arms. Robot-canopy non-interference geometric constraints were introduced in the simulator, to determine the closest position of the arms' base frames with respect to the trees, inside an orchard row. Also, design constraints for such arms, such as maximum reach, size and type of the gripper, and range of approach directions, were introduced to estimate the effect of each of these constraints on the LFR. Simulations results showed that 85.5% of pears were reachable after harvesting consecutively, at three different approach angles ('passes') with a gripper of size 11 cm and an arm extension of 150 cm. For peaches, after three passes, 83.5% were reachable with a gripper size of 11 cm and an arm extension of 200 cm. LFR increased as the gripper's size approached the maximum fruit size and decreased thereafter. Also, retractive grippers on linear arms yielded more fruit compared to vacuum-tube type grippers. Overall, the results suggested that telescoping arms can be used to harvest certain types of SNAP-style trees. Also, this methodology can be used to guide the design of robotic harvesters, as well as the canopy management practices of fruit trees.
A Study of Fruit Reachability in Orchard Trees by Linear-Only Motion
IFAC-PapersOnLine, 2016
Robotic fruit harvesters typically utilize multiple-degree-of-freedom arms, often kinematically redundant. The hypothesis is that as branches constrain fruit reachability, redundancy is necessary to navigate through branches and reach fruits inside the canopy. Modern commercial orchards increasingly adopt trees of SNAP architectures (Simple, Narrow, Accessible, and Productive). This paper presents a simulation study on linear fruit reachability (LFR) on high-density, trellised pear trees; linear only motion was used to reach the fruits. Results based on digitized geometric tree models and fruit locations showed that 91.1% of the fruits were reachable after three "harvesting passes" with proper approach angles.
Next generation image guided citrus fruit picker
2012
Supply and availability of labor for fruit harvesting is a problem worldwide, and attempts to mechanize the fruit harvesting process have been carried out for decades. During the last century, fruit harvesters have tried mechanical methods such as trunk shaking, canopy shaking, raking, and mass mechanical penetration. Though they are economical alternative options for harvesting nuts, olives, cherries, and prunes, these brute-force mechanical methods have had limited results for citrus fruit. Robotics has also been tried. Technical challenges in robotics include recognizing and locating the fruit and detaching it according to prescribed criteria without damaging either the fruit or the tree. The key practical problem, though, is economic. A robotic system needs to be economically sound to warrant its use as an alternative to hand picking. Cambridge, Massachusetts, based Energid Technologies Corporation is developing a practical robotic fruit picking system whose concept is driven by the economics of mass harvesting while leveraging machine vision and robotic guidance and control. The system uses flexible tubes with removal tools at one end that can be individually fired pneumatically and steered robotically, with sensor input coming from a grid of machine vision cameras.
Review on “ Automation in Fruit Harvesting ”
2015
The challenges in developing a fruit harvesting robot are recognizing the fruit in the foliage and detaching the fruit from the tree without damaging both the fruit and the tree. In large-scale greenhouse production, technological developments can reduce production costs; mechanization of crop maintenance and harvesting is one desirable way to accomplish this. Over the last several years there has been a renewed interest in the automation of harvesting of fruits and vegetables. The objectives of this study were to develop a real-time fruit detection system using machine vision and laser ranging sensor and to develop an end effector capable of detaching the fruit similar to the human picker. This paper deals with fruit recognition and it presents the development of a various techniques for the harvesting of fruits.
Design optimization and trajectory planning of a strawberry harvesting manipulator
Bulletin of Electrical Engineering and Informatics, 2024
This paper presents a systematic approach to optimizing the structural parameters of a 4-degree-of-freedom (DoF) strawberry harvesting manipulator to minimize its workspace. Unlike previous research that primarily concentrated on the spatial needs related to fruit distribution areas, this work addresses the spatial dynamics of different stages of the fruitpicking process. This is achieved by combining the workspace model method, mathematical modeling, and the GlobalSearch algorithm in the optimization process. A comprehensive verification was conducted using the Denavit-Hartenberg method to simulate the workspace of the optimal manipulator structure. This ensured that the manipulator effectively covered the entire harvesting space. The research design involves exploring an optimal trajectory planning method by adopting a modified sine jerk profile that minimizes overall trajectory duration while maintaining good smoothness. The effectiveness of this method is demonstrated through a simulation of the trajectory of the four joints to drive the end effector from the initial position to the position of the strawberry. This approach yields execution times up to 27% shorter than in previous studies. The proposed method is useful for optimizing the physical and trajectory design of the harvesting manipulator that operates in confined and restricted environments to enhance efficiency, adaptability, and safety in harvesting operations
Development of a sweet pepper harvesting robot
Journal of Field Robotics
This paper presents the development, testing and validation of SWEEPER, a robot for harvesting sweet pepper fruit in greenhouses. The robotic system includes a six degrees of freedom industrial arm equipped with a specially designed end effector, RGB-D camera, high-end computer with graphics processing unit, programmable logic controllers, other electronic equipment, and a small container to store harvested fruit. All is mounted on a cart that autonomously drives on pipe rails and concrete floor in the end-user environment. The overall operation of the harvesting robot is described along with details of the algorithms for fruit detection and localization, grasp pose estimation, and motion control. The main contributions of this paper are the integrated system design and its validation and extensive field testing in a commercial greenhouse for different varieties and growing conditions. A total of 262 fruits were involved in a 4-week long testing period. The average cycle time to harvest a fruit was 24 s. Logistics took approximately 50% of this time (7.8 s for discharge of fruit and 4.7 s for platform movements). Laboratory experiments have proven that the cycle time can be reduced to 15 s by running the robot manipulator at a higher speed. The harvest success rates were 61% for the best fit crop conditions and 18% in current crop conditions. This reveals the importance of finding the best fit crop conditions and crop varieties for successful robotic harvesting. The SWEEPER robot is the first sweet pepper harvesting robot to demonstrate this kind of performance in a commercial greenhouse.
Optimal conceptual design and vision-based control of a fruit harvesting robot
International Journal of Intelligent Machines and Robotics, 2018
The main contribution of this paper is to develop a vision-based control of a robotic arm for the harvesting fruits. The camera fixed in the gripper pad enables to precisely locate the fruit and pluck it from the branch. Rigorous stability analysis is done to ensure the guaranteed performance of the closed loop system. The camera feedback locates the exact position of the fruit; this enables the controller to track a suitable and optimal path to reach the target by performing desirable transformations. The manipulator with five-DOF (RRPRR) is designed and optimised for the formulating simple control strategies. The finger-like built gripper is electrically actuated to provide the necessary force required in harvesting the fruit. Also an additional bellow kind of structure is specially designed and located below the gripper which helps to roll down the harvested fruit on to the storage container without damaging it. Numerical simulation analysis was carried out along with the design realisation to justify the context. The advancement in the field of agrionics has also been a source of inspiration in designing agricultural robots. 46 K. Saran Kumar et al.
Development of an Autonomous Multifunctional Fruits Harvester
Open Access Journal of Agricultural Research
There is global shortage of food production due to inadequate workforce, to carry out crop production. This study strived to develop an autonomous system, which can harvest more than one type of crop with high accuracy to improve effectiveness, and minimized production and operational costs. To minimize the mechanical damage done to the fruits during the harvesting and handling operations, the system combined the mechanical properties and optical properties of the fruits during operation. Sensor was attached to the fruits collection container system to prevent overloading of the container, hence protecting the fruits from mechanical stresses. Results obtained for the laboratory trial revealed that the prototype robotic system had a higher performance (about 90%) when tested with pepper fruits, when compared with the performance rating (about 85%) recorded with eggplant fruits. It was also observed from the laboratory trials that the robotic system aborted the harvesting operation, w...