9,272 research outputs found
Collision-free inverse kinematics of the redundant seven-link manipulator used in a cucumber picking robot
The paper presents results of research on an inverse kinematics algorithm that has been used in a functional model of a cucumber-harvesting robot consisting of a redundant P6R manipulator. Within a first generic approach, the inverse kinematics problem was reformulated as a non-linear programming problem and solved with a Genetic Algorithm (GA). Although solutions were easily obtained, the considerable calculation time needed to solve the problem prevented on-line implementation. To circumvent this problem, a second, less generic, approach was developed which consisted of a mixed numerical-analytic solution of the inverse kinematics problem exploiting the particular structure of the P6R manipulator. Using the latter approach, calculation time was considerably reduced. During the early stages of the cucumber-harvesting project, this inverse kinematics algorithm was used off-line to evaluate the ability of the robot to harvest cucumbers using 3D-information obtained from a cucumber crop in a real greenhouse. Thereafter, the algorithm was employed successfully in a functional model of the cucumber harvester to determine if cucumbers were hanging within the reachable workspace of the robot and to determine a collision-free harvest posture to be used for motion control of the manipulator during harvesting. The inverse kinematics algorithm is presented and demonstrated with some illustrative examples of cucumber harvesting, both off-line during the design phase as well as on-line during a field test
Human Body Posture Recognition Approaches: A Review
Human body posture recognition has become the focus of many researchers in recent years. Recognition of body posture is used in various applications, including surveillance, security, and health monitoring. However, these systems that determine the body’s posture through video clips, images, or data from sensors have many challenges when used in the real world. This paper provides an important review of how most essential ‎ hardware technologies are ‎used in posture recognition systems‎. These systems capture and collect datasets through ‎accelerometer sensors or computer vision. In addition, this paper presents a comparison ‎study with state-of-the-art in terms of accuracy. We also present the advantages and ‎limitations of each system and suggest promising future ideas that can increase the ‎efficiency of the existing posture recognition system. Finally, the most common datasets ‎applied in these systems are described in detail. It aims to be a resource to help choose one of the methods in recognizing the posture of the human body and the techniques that suit each method. It analyzes more than 80 papers between 2015 and 202
SDR-GAIN: A High Real-Time Occluded Pedestrian Pose Completion Method for Autonomous Driving
To mitigate the challenges arising from partial occlusion in human pose
keypoint based pedestrian detection methods , we present a novel pedestrian
pose keypoint completion method called the separation and dimensionality
reduction-based generative adversarial imputation networks (SDR-GAIN) .
Firstly, we utilize OpenPose to estimate pedestrian poses in images. Then, we
isolate the head and torso keypoints of pedestrians with incomplete keypoints
due to occlusion or other factors and perform dimensionality reduction to
enhance features and further unify feature distribution. Finally, we introduce
two generative models based on the generative adversarial networks (GAN)
framework, which incorporate Huber loss, residual structure, and L1
regularization to generate missing parts of the incomplete head and torso pose
keypoints of partially occluded pedestrians, resulting in pose completion. Our
experiments on MS COCO and JAAD datasets demonstrate that SDR-GAIN outperforms
basic GAIN framework, interpolation methods PCHIP and MAkima, machine learning
methods k-NN and MissForest in terms of pose completion task. In addition, the
runtime of SDR-GAIN is approximately 0.4ms, displaying high real-time
performance and significant application value in the field of autonomous
driving
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