Fruit Harvesting Robot Research Articles

A neural-network-based approach to the double traveling salesman problem.

The double traveling salesman problem is a variation of the basic traveling salesman problem where targets can be reached by two salespersons operating in parallel. The real problem addressed by this work concerns the optimization of the harvest sequence for the two independent arms of a fruit-harvesting robot. This application poses further constraints, like a collision-avoidance function. The proposed solution is based on a self-organizing map structure, initialized with as many artificial neurons as the number of targets to be reached. One of the key components of the process is the combination of competitive relaxation with a mechanism for deleting and creating artificial neurons. Moreover, in the competitive relaxation process, information about the trajectory connecting the neurons is combined with the distance of neurons from the target. This strategy prevents tangles in the trajectory and collisions between the two tours. Results of tests indicate that the proposed approach is efficient and reliable for harvest sequence planning. Moreover, the enhancements added to the pure self-organizing map concept are of wider importance, as proved by a traveling salesman problem version of the program, simplified from the double version for comparison.

A vision system based on a laser range-finder applied to robotic fruit harvesting

This paper describes a laser-based computer vision system used for automatic fruit recognition. It is based on an infrared laser range-finder sensor that provides range and reflectance images and is designed to detect spherical objects in non-structured environments. Image analysis algorithms integrate both range and reflectance information to generate four characteristic primitives which give evidence of the existence of spherical objects. The output of this vision system includes 3D position, radius and surface reflectivity of each spherical object. It has been applied to the AGRIBOT orange harvesting robot, where it has obtained good fruit detection rates and unlikely false detections.

Fruit Harvesting Robotics

Many robotics techniques have been applied in agriculture in Japan, including fruit harvesting robots for tomatoes, cherry tomatoes, cucumbers, strawberries, and grapes. Work at Okayama University has Involved manipulators, end-cffcctors, visual sensors, and traveling components based on biological objects to give robots robustness and flexbiliy. Horticultural approachcs such as selecting plant varieties and changing training systems adaptable to robot use arc needed, however. We studied components and functions for fruit-harvesting robots.

Fruit harvesting robots in Japan

We have developed harvesting robots for tomato /1/, petty-tomato, cucumber /2/ and grape /3/ in Japan. These robots mainly consist of manipulators, end-effectors, visual sensors and traveling devices. These mechanisms of the robot components were developed based on the physical properties of the work objects. The robots must work automatically by themselves in greenhouses or fields, since we are considering for one operator to tend several robots in the production system. The system is modeled after Japanese agriculture which is commonly seen to produce many kinds of crops in greenhouses and in many small fields intensively. Bioproduction in space is somewhat similar to the agricultural system in Japan, because few operators have to work in a small space. Employing robots for bioproduction in space is considered desirable in near future. The following is a description of the harvesting robots.

Design of an autonomous agricultural robot

This paper presents a state-of-the-art review in the development of autonomous agricultural robots including guidance systems, greenhouse autonomous systems and fruit-harvesting robots. A general concept for a field crops robotic machine to selectively harvest easily bruised fruit and vegetables is designed. Future trends that must be pursued in order to make robots a viable option for agricultural operations are focused upon.

Near-minimum-time task planning for fruit-picking robots

A near-minimum-time task-planning algorithm for fruit-harvesting 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 proposed algorithm 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.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Color Vision in Robotic Fruit Harvesting

ABSTRACT THIS report discusses the use of chrominance information in natural scenes to enhance digital color images used to control a robotic manipulator for fruit harvest. By exploiting the natural high contrast in color between fruit and other objects in a color image the ability to differentiate citrus fruit from background leaves, soil, and sky is demonstrated.