Fast and Resilient Manipulation Planning for Object Retrieval in Cluttered and Confined Environments

2021 ◽  
pp. 1-14
Author(s):  
Changjoo Nam ◽  
Sang Hun Cheong ◽  
Jinhwi Lee ◽  
Dong Hwan Kim ◽  
ChangHwan Kim
Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2280
Author(s):  
Ching-Chang Wong ◽  
Li-Yu Yeh ◽  
Chih-Cheng Liu ◽  
Chi-Yi Tsai ◽  
Hisasuki Aoyama

In this paper, a manipulation planning method for object re-orientation based on semantic segmentation keypoint detection is proposed for robot manipulator which is able to detect and re-orientate the randomly placed objects to a specified position and pose. There are two main parts: (1) 3D keypoint detection system; and (2) manipulation planning system for object re-orientation. In the 3D keypoint detection system, an RGB-D camera is used to obtain the information of the environment and can generate 3D keypoints of the target object as inputs to represent its corresponding position and pose. This process simplifies the 3D model representation so that the manipulation planning for object re-orientation can be executed in a category-level manner by adding various training data of the object in the training phase. In addition, 3D suction points in both the object’s current and expected poses are also generated as the inputs of the next operation stage. During the next stage, Mask Region-Convolutional Neural Network (Mask R-CNN) algorithm is used for preliminary object detection and object image. The highest confidence index image is selected as the input of the semantic segmentation system in order to classify each pixel in the picture for the corresponding pack unit of the object. In addition, after using a convolutional neural network for semantic segmentation, the Conditional Random Fields (CRFs) method is used to perform several iterations to obtain a more accurate result of object recognition. When the target object is segmented into the pack units of image process, the center position of each pack unit can be obtained. Then, a normal vector of each pack unit’s center points is generated by the depth image information and pose of the object, which can be obtained by connecting the center points of each pack unit. In the manipulation planning system for object re-orientation, the pose of the object and the normal vector of each pack unit are first converted into the working coordinate system of the robot manipulator. Then, according to the current and expected pose of the object, the spherical linear interpolation (Slerp) algorithm is used to generate a series of movements in the workspace for object re-orientation on the robot manipulator. In addition, the pose of the object is adjusted on the z-axis of the object’s geodetic coordinate system based on the image features on the surface of the object, so that the pose of the placed object can approach the desired pose. Finally, a robot manipulator and a vacuum suction cup made by the laboratory are used to verify that the proposed system can indeed complete the planned task of object re-orientation.


2005 ◽  
Vol 41 (4) ◽  
pp. 179 ◽  
Author(s):  
J.-L. Shih ◽  
C.-H. Lee ◽  
J.T. Wang

Author(s):  
H Liu ◽  
J S Dai ◽  
H Y Xu ◽  
H Li

This paper proposes a new approach for analysing cooperative manipulation in which cooperative manipulators form a mechanism closure that allows a virtual-mechanism-based analysis to take place. The method is based on the geometry of manipulators during manipulation and converts the cooperative manipulation problem into the analysis of a hypothetical mechanism so that the mechanism theory can be used for the manipulation. This mechanism is hence generated by the fact that the end points (or geometric centres of respective grippers) of cooperative manipulators coincide with a virtual joint during cooperative manipulation. The analysis not only generates positions and orientations of the end effectors of cooperative manipulators but also produces corresponding link configurations that can be used for manipulation planning. The approach is further used for the orientation-based trajectory planning with two different cases. Simulations and discussions are made with respect to cooperative manipulations using two 2R manipulators and one 2R manipulators and one 3R manipulator.


1997 ◽  
Vol 45 (6) ◽  
pp. 490-492 ◽  
Author(s):  
Douglas O. Faigel ◽  
Brian R. Stotland ◽  
Michael L. Kochman ◽  
Timothy Hoops ◽  
Thomas Judge ◽  
...  

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