Planning Grasps With Suction Cups and Parallel Grippers Using Superimposed Segmentation of Object Meshes

Wan, Weiwei; Harada, Kensuke; Kanehiro, Fumio

doi:10.1109/tro.2020.3014036

Cited by 43 publications

(21 citation statements)

References 53 publications

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“…Combining the two scores, we can identify the specific model with the highest similarity to the target object from the existing database. With this model, we apply a surface segmentation method [21] to preplan a series of robust grasps on it. With these grasps, we use the transformation matrix obtained from point cloud registration to transfer them from the model to the target object, and develop an optimization algorithm to minimize the transfer error.…”

Section: Similarity Prediction a System Overviewmentioning

confidence: 99%

Category-Association Based Similarity Matching for Novel Object Pick-and-Place Task

Chen,

Kiyokawa,

Wan

et al. 2022

Preprint

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Robotic pick-and-place has been researched for a long time to cope with uncertainty of novel objects and changeable environments. Past works mainly focus on learningbased methods to achieve high precision. However, they have difficulty being generalized for the limitation of specified training models. To break through this drawback of learning-based approaches, we introduce a new perspective of similarity matching between novel objects and a known database based on categoryassociation to achieve pick-and-place tasks with high accuracy and stabilization. We calculate the category name similarity using word embedding to quantify the semantic similarity between the categories of known models and the target real-world objects. With a similar model identified by a similarity prediction function, we preplan a series of robust grasps and imitate them to plan new grasps on the real-world target object. We also propose a distance-based method to infer the in-hand posture of objects and adjust small rotations to achieve stable placements under uncertainty. Through a real-world robotic pick-and-place experiment with a dozen of in-category and out-of-category novel objects, our method achieved an average success rate of 90.6% and 75.9% respectively, validating the capacity of generalization to diverse objects.

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Section: Similarity Prediction a System Overviewmentioning

confidence: 99%

Category-Association Based Similarity Matching for Novel Object Pick-and-Place Task

Chen,

Kiyokawa,

Wan

et al. 2022

Preprint

Self Cite

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“…Moreover, the weak stiffness of the finger results in the non-ignorable impact of the gravity during the finger's bending process, which aggravates the nonlinearity of the bending motion, especially when the bending angle getting big and performing high gravity bending moment. The bending angles tracking the reference trajectory were recorded by a six-axis gyroscope (MPU6050) [28,29] . Figure 12 shows the angular response with respect to sinusoidal with the frequency of 0.2 Hz and 0.4 Hz, respectively.…”

Section: Motion Performance Of the Fingermentioning

confidence: 99%

Pneumatic webbed soft gripper for unstructured grasping

Cai

Tang

Pan

et al. 2021

International Journal of Agricultural and Biological Engineering

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Grasping unstructured and fragile objects such as food and fruits is a great challenge for robots. Being naturally different from the traditional rigid robot, soft robotics provide highly promising choices with their intrinsic flexibility and compliance to objects. Inspired by duck foot and octopus tentacle, a pneumatic webbed soft gripper was proposed, which is consisted of four multi-chambered fingers and four webs. Due to its silicone body and soft web structure, the developed soft gripper can naturally adapt, grasp and hold delicate and unstructured objects. Compressed air inflated into the three chambers of the finger actuates the silicone body and performs inflection and extension. The silicone web follows the motion of four fingers, forming a semi-closed grasping configuration. The fingers were fabricated with silicone rubber and constraint spring by casting process. The web was cast around the fingers. The inflecting motion was modeled via the pneumatic principle and geometrical analysis. The dynamic properties of the finger were tested by step and sinusoidal signals. And the grasping performances for different objects, such as egg, strawberry, candy, and knife, were also demonstrated by experiments. The proposed soft gripper performed stably in response to a 0.4 Hz reference sinusoidal signal. The bionic structure greatly improves the stability and reliability of grasping, particularly for unstructured and fragile objects. Moreover, the webs ensure the grasping for multiple objects in one snatch, especially suitable for agricultural products and food processing.

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“…The grasp planning method varies for different object placement, and the grasp planning method considered in this paper is shown in Algorithm 2. For objects regularly placed in the tray, model-based grasp planner [35], [36] can be used to prepare a set of grasps for the target object O j in the tray in the offline phase (line 2 and 3). For objects randomly placed in the tray, we treat the objects in the tray as a whole and use a model-free grasp planning method [37] to estimate a set of grasps from depth images (line 5), the details are described in Section VII-B.…”

Section: A Task Defined As Reaching the Grasping Posesmentioning

confidence: 99%

Planning a Minimum Sequence of Positions for Picking Parts From Multiple Trays Using a Mobile Manipulator

Xu¹,

Domae

Ueshiba

et al. 2021

IEEE Access

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Mobile manipulators are able to operate in a large workspace, and have the potential to replace human workers to perform a sequence of pick-and-place tasks at separate locations. Many existing works optimize the base position or manipulator configuration for a single manipulation task, however, very few of them consider a sequence of tasks. In this paper, we present a planner that plans a minimum sequence of base positions for a mobile manipulator to robustly collect objects stored in multiple trays. We use inverse kinematics to determine the base region where a mobile manipulator can grasp the target objects stored in a tray, and move the mobile manipulator to the intersections of base regions to reduce the operation time for moving the base. We ensure robustness by only considering the intersection whose radius of the inscribed circle is larger than the base positioning error. Then the minimization of the number of base positions is formulated as a 0-1 knapsack problem. Besides, considering different object placements in the tray, we analyze feasible policies for dynamically updating the base sequence based on either the remaining objects or the target objects to be picked. In the experiment, we examine our planner on various scenarios, including different object placements: (1) Regularly placed toy objects; (2) Randomly placed industrial parts; and different implementation policies: (1) Apply globally static base positions; (2) Dynamically update the base positions. The experiment results show that the time for moving the base decreases by 11.22 seconds (29.37%) to 17.26 seconds (36.77%) by reducing one base movement, and demonstrate the feasibility and potential of the proposed method.INDEX TERMS Mobile manipulation, manufacturing automation, part-supply tasks, pick-and-place, tasklevel planning.

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Planning Grasps With Suction Cups and Parallel Grippers Using Superimposed Segmentation of Object Meshes

Cited by 43 publications

References 53 publications

Category-Association Based Similarity Matching for Novel Object Pick-and-Place Task

Category-Association Based Similarity Matching for Novel Object Pick-and-Place Task

Pneumatic webbed soft gripper for unstructured grasping

Planning a Minimum Sequence of Positions for Picking Parts From Multiple Trays Using a Mobile Manipulator

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