Vision-based robotic grasping from object localization, object pose estimation to grasp estimation for parallel grippers: a review

Du, Guoguang; Wang, Kai; Lian, Shiguo; Zhao, Kaixuan

doi:10.1007/s10462-020-09888-5

Cited by 322 publications

(145 citation statements)

References 268 publications

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“…Some surveys elaborate the perception techniques of robotics, and some others introduce approaches of robot manipulation. Ruiz-del-Solar et al ( 2018 ) and Du et al ( 2020 ) pay attention to the vision methods for robot manipulations. With the exception of visual perception approaches, Luo et al ( 2017 ) and Wang C. et al ( 2020 ) exhibit there are many other perception methods can help improve robot performance.…”

Section: Proposed Taxonomymentioning

confidence: 99%

Robotics Dexterous Grasping: The Methods Based on Point Cloud and Deep Learning

et al. 2021

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Dexterous manipulation, especially dexterous grasping, is a primitive and crucial ability of robots that allows the implementation of performing human-like behaviors. Deploying the ability on robots enables them to assist and substitute human to accomplish more complex tasks in daily life and industrial production. A comprehensive review of the methods based on point cloud and deep learning for robotics dexterous grasping from three perspectives is given in this paper. As a new category schemes of the mainstream methods, the proposed generation-evaluation framework is the core concept of the classification. The other two classifications based on learning modes and applications are also briefly described afterwards. This review aims to afford a guideline for robotics dexterous grasping researchers and developers.

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Section: Proposed Taxonomymentioning

confidence: 99%

Robotics Dexterous Grasping: The Methods Based on Point Cloud and Deep Learning

et al. 2021

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“…This process is learned by humans in early years. Grasping methods in robotics have approached the problem using mainly computer-vision as sensory input [ 4 ], even when dealing with previously unknown objects [ 5 ]. Machine learning has been applied increasingly in recent years for improving the robustness under changes in environment and illumination [ 6 ] or for dealing with multiple randomly oriented or piled up objects [ 7 , 8 ] but keeping vision as the main sensory input.…”

Section: Introductionmentioning

confidence: 99%

Dataset of Tactile Signatures of the Human Right Hand in Twenty-One Activities of Daily Living Using a High Spatial Resolution Pressure Sensor

Cepriá-Bernal

Pérez-González

2021

Sensors

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Successful grasping with multi-fingered prosthetic or robotic hands remains a challenge to be solved for the effective use of these hands in unstructured environments. To this end, currently available tactile sensors need to improve their sensitivity, robustness, and spatial resolution, but a better knowledge of the distribution of contact forces in the human hand in grasping tasks is also necessary. The human tactile signatures can inform models for an efficient control of the artificial hands. In this study we present and analyze a dataset of tactile signatures of the human hand in twenty-one representative activities of daily living, obtained using a commercial high spatial resolution pressure sensor. The experiments were repeated for twenty-two subjects. The whole dataset includes more than one hundred million pressure data. The effect of the task and the subject on the grip force and the contribution to this grip force made by the different hand regions were analyzed. We also propose a method to effectively synchronize the measurements from different subjects and a method to represent the tactile signature of each task, highlighting the hand regions mainly involved in the task. The correlations between hand regions and between different tasks were also analyzed.

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“…From a mechanical point of view, the aerial manipulator must embed a versatile mechanism that can dexterously perform a range of gripping and manipulation. In general, a conventional gripper follows a specific sequence (i.e., approaching the target, gripping and manipulation, and releasing) and needs a grasping strategy that incorporates proprioception to perform multidimensional motions [20]. However, the grasping strategy requires prior knowledge of the target, which requires a complex control system and a high computational load.…”

Section: Introductionmentioning

confidence: 99%

Deployable Hook Retrieval System for UAV Rescue and Delivery

Nguyen

Jung

et al. 2021

IEEE Access

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The rapid development of unmanned aerial vehicles (UAVs) has helped expand their practical use to many industrial applications. However, UAVs sometimes suffer from a flight time limitation and/or a loss in communication. Such undesired malfunctions can endanger public safety and incur economic losses. This paper presents a new class of UAV that can retrieve a disabled or malfunctioned UAV from the ground. We developed a deployable hook retrieval system (DHRS) which integrates three principal mechanisms (i.e., deployment, slider-linkage-release, and hook release). Each mechanism plays a role in deploying and retrieving multiple hooks while using a simple control strategy. Through a Finite Element Method simulation, the hook was topologically optimized in order to achieve a high strength while reducing weight. The deployed multiple hooks allow the device to capture the target regardless of its orientation. Due to these design strategies, object recognition using a computer vision was simply demonstrated by exploiting ORB and FLANN algorithms. Through an experimental study, we discussed the target range, success rate, and the practical uses that the DHRS could achieve. The results show that the proposed designs were versatile and consistently successful in capturing the targets while addressing constraints such as power consumption, computational load, and lack of prior knowledge or information about the target.

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Vision-based robotic grasping from object localization, object pose estimation to grasp estimation for parallel grippers: a review

Cited by 322 publications

References 268 publications

Robotics Dexterous Grasping: The Methods Based on Point Cloud and Deep Learning

Robotics Dexterous Grasping: The Methods Based on Point Cloud and Deep Learning

Dataset of Tactile Signatures of the Human Right Hand in Twenty-One Activities of Daily Living Using a High Spatial Resolution Pressure Sensor

Deployable Hook Retrieval System for UAV Rescue and Delivery

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