Search Results (7)

Aiming to solve the problem that most existing apple-picking robots operate with a single arm and that the overall efficiency of the machine needs to be further improved, a prototype of a dual-arm picking robot was built, and its picking operation planning method was studied. Firstly, based on the configuration and motion mode of the AUBO-i5 robotic arm, the overlapping dual-arm layout of the workspace was determined. Then, a prototype of a dual-arm apple-picking robot was built, and, based on the designed dual-arm spatial layout, a dual-arm picking operation zoning planning method was proposed. The experimental results showed that in the four simulation experiments, the highest value of the maximum parallel operation proportion of the dual arms was 83%, and the lowest value was 50.6%. The highest value of the maximum operation length of the single arm was 7323 mm, and the lowest value was 5654 mm. The total length of the dual-arm operation path was 12,705 mm, and the lowest value was 8770 mm. Furthermore, a fruit-picking sequence planning method based on dual robotic arm operation was proposed. Fruit traversal simulation verification experiments were conducted. The results showed that there was no conflict between the left and right arms during the motion of the dual robotic arms. Finally, the proposed dual-arm robot operation zoning and picking sequence planning method was validated in the apple experimental station. The results showed that the proportion of dual-arm parallel operations was the lowest at 50.7% and the highest at 72.4%. The total length of the dual-arm operation path was the highest at 8604 mm and the lowest at 6511 mm. Full article

The use of multi-joint arms in a high-spindle environment can solve complex problems, but the singularity problem of the manipulator related to the structure of the serial manipulator is prominent. Therefore, based on the general mathematical model of fruit spatial distribution in high-spindle apple orchards, this study proposes two harvesting system architecture schemes that can meet the constraints of fruit spatial distribution and reduce the singularity of harvesting robot operation, which are four-arm dual-module independent moving scheme (Scheme A) and four-arm single-module parallel moving scheme (Scheme B). Based on the link-joint method, the analytical expression of the singular configuration of the redundant degree of freedom arm group system under the two schemes is obtained. Then, the inverse kinematics solution method of the redundant arm group and the singularity avoidance picking trajectory planning strategy are proposed to realize the judgment and solution of the singular configuration in the complex working environment of the high-spindle. The singularity rate of Scheme A in the simulation environment is 17.098%, and the singularity rate of Scheme B is only 6.74%. In the field experiment, the singularity rate of Scheme A is 26.18%, while the singularity rate of Scheme B is 13.22%. The success rate of Schemes A and B are 80.49% and 72.33%, respectively. Through experimental comparison and analysis, Scheme B is more prominent in solving singular problems but still needs to improve the success rate in future research. This paper can provide a reference for solving the singular problems in the complex working environment of high spindles. Full article

Robotic harvesting has become an urgent need for the development of the apple industry, due to the sharp decline in agricultural labor. At present, harvesting apples using robots in unstructured orchard environments remains a significant challenge. This paper focuses on addressing the challenges of perception, localization, and dual-arm coordination in harvesting robots and presents a dual-arm apple harvesting robot system. First, the paper introduces the integration of the robot’s hardware and software systems, as well as the control system architecture, and describes the robot’s workflow. Secondly, combining a dual-vision perception system, the paper adopts a fruit recognition method based on a multi-task network model and a frustum-based fruit localization approach to identify and localize fruits. Finally, to improve collaboration efficiency, a multi-arm task planning method based on a genetic algorithm is used to optimize the target harvesting sequence for each arm. Field experiments were conducted in an orchard to evaluate the overall performance of the robot system. The field trials demonstrated that the robot system achieved an overall harvest success rate of 76.97%, with an average fruit picking time of 7.29 s per fruit and a fruit damage rate of only 5.56%. Full article

Intermittent stop–move motion planning is essential for optimizing the efficiency of harvesting robots in greenhouse settings. Addressing issues like frequent stops, missed targets, and uneven task allocation, this study introduced a novel intermittent motion planning model using deep reinforcement learning for a dual-arm harvesting robot vehicle. Initially, the model gathered real-time coordinate data of target fruits on both sides of the robot, and projected these coordinates onto a two-dimensional map. Subsequently, the DDPG (Deep Deterministic Policy Gradient) algorithm was employed to generate parking node sequences for the robotic vehicle. A dynamic simulation environment, designed to mimic industrial greenhouse conditions, was developed to enhance the DDPG to generalize to real-world scenarios. Simulation results have indicated that the convergence performance of the DDPG model was improved by 19.82% and 33.66% compared to the SAC and TD3 models, respectively. In tomato greenhouse experiments, the model reduced vehicle parking frequency by 46.5% and 36.1% and decreased arm idleness by 42.9% and 33.9%, compared to grid-based and area division algorithms, without missing any targets. The average time required to generate planned paths was 6.9 ms. These findings demonstrate that the parking planning method proposed in this paper can effectively improve the overall harvesting efficiency and allocate tasks for a dual-arm harvesting robot in a more rational manner. Full article

In harvesting operations, simulation verification of hand–eye coordination in a virtual canopy is critical for harvesting robot research. More realistic scenarios, vision-based driving motion, and cross-platform interaction information are needed to achieve such simulations, which are very challenging. Current simulations are more focused on path planning operations for consistency scenarios, which are far from satisfying the requirements. To this end, a new approach of visual servo multi-interaction simulation in real scenarios is proposed. In this study, a dual-arm grape harvesting robot in the laboratory is used as an example. To overcome these challenges, a multi-software federation is first proposed to establish their communication and cross-software sending of image information, coordinate information, and control commands. Then, the fruit recognition and positioning algorithm, forward and inverse kinematic model and simulation model are embedded in OpenCV and MATLAB, respectively, to drive the simulation run of the robot in V-REP, thus realizing the multi-interaction simulation of hand–eye coordination in virtual trellis vineyard. Finally, the simulation is verified, and the results show that the average running time of a string-picking simulation system is 6.5 s, and the success rate of accurate picking point grasping reached 83.3%. A complex closed loop of “scene-image recognition-grasping” is formed by data processing and transmission of various information. It can effectively realize the continuous hand–eye coordination multi-interaction simulation of the harvesting robot under the virtual environment. Full article

In order to ensure canopy area coverage with the most compact mechanical configuration possible, this paper proposes a configuration optimization design method of dual-manipulator to meet the research and development needs of an apple-efficient harvesting robot using the typical tree shape of a “high spindle” in China as the object. A Cartesian coordinate dual-manipulator with two groups of vertically synchronous operations and a three-degree range of motion based on the features of the spatial distribution of fruits under a typical canopy of dwarf and close planting was designed. Two-stage telescoping components that can be driven by both gas and electricity are employed to ensure the picking robotic arm’s quick response and accessibility to the tree crown. Based on the quantitative description of the working space and configuration parameters of the dual-manipulator, a multi-objective optimization model of the major configuration parameters is constructed. A comprehensive evaluation method of the dual-manipulator configuration based on the CRITIC–TOPSIS combined method is proposed. The optimal solutions of the lengths and elevations of upper and lower telescopic parts of the dual-manipulator and the distance from the mounting base of the outer frame of the dual-manipulator to the center of the tree trunk are determined, which are 1119.3 mm and 39.4°, 898.7 mm and 26°, 755.3 mm, respectively. The interaction between the configuration parameters of the dual-manipulator and its working area is then simulated and examined in order to verify the rationality of the optimum configuration settings. The results show that the optimal configuration of the dual-manipulator can fully cover the target working space, and the redundancy rate is 16.62%. The results of this study can be utilized to advance robotic fruit-picking research and development. Full article

Double-arm picking robots are widely used in agricultural production for their high collaborative efficiency. While picking, area planning and collision detection between the mechanical arms is a crucial challenge for the double-arm robot, which needs to establish a collision-free path for fruit picking. In this study, we developed a double-arm cooperation method for robotic picking of kiwifruit. Firstly, the problem of dividing the picking area was simplified into a multiple traveling salesmen problem (MTSP) to be solved. The picking sequence of each robotic arm was formulated by the principle of similar picking numbers, and combined with the brainstorming optimization algorithm (BSO). Secondly, a double-arm parameter model was built to solve the forward and backward movements of the robotic arms and to figure out the joint position. The spatial mathematical relationship of the bounding boxes between the robotic arms was used to detect the collision between the two robotic arms, in order to achieve the avoidance between the robotic joints. Then, simulation software was applied to the simulation and analyzed the availability of picking area planning and collision detection. The simulation results showed that the optimized picking sequence planning using BSO was more efficient; the smooth joint trajectory during the movement of the robotic arms met the limits on the range of movement and on the angular velocity of the robotic arm joints. Finally, based on the simulation result, a double-arm collaboration platform was tested. The double-arm collaboration platform harvesting trials showed that the average picking success rate was 86.67%, and collision detection time was 3.95 ± 0.83 s per fruit. These results indicated that the proposed method could plan the operation tasks of the double-arm picking robot system, and effectively implement the collision-free picking operation. Full article