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Deep Robotic Grasping Prediction with Hierarchical RGB-D Fusion
Yaoxian Song,Jun Wen,Dongfang Liu,Changbin Yu 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.1
Vision-based robotic grasping is a fundamental task in robotic control. Dexterous and precise grasp control of the robotic arm is challenging and a critical technique for the manufacturing and emerging robot service industry. Current state-of-art methods adopt RGB-D images or point clouds in an attempt to obtain an accurate, robust, and real-time policy. However, most of these methods only use single modal data or ignore the uncertainty of sampling data especially the depth information. Even they leverage multi-modal data, they seldom fuse the features in different scales. All of these results in unreliable grasp prediction inevitably. In this paper, we propose a novel multi-modal neural network to predict grasps in real-time. The key idea is to fuse RGB and depth information hierarchically and quantify the uncertainty of raw depth data to re-weight the depth features. For higher grasping performance, a background extraction module and depth re-estimation module are used to reduce the influence caused by the incompletion and low-quality of the raw data. We evaluate the performance on the Cornell Grasp Dataset and provide a series of extensive experiments to demonstrate the advantages of our method on a real robot. The results indicate the superiority of our proposed method by outperforming the state-of-the-art methods significantly in all metrics.
Jiangping Wang,Shirong Liu,Botao Zhang,Changbin Yu 제어·로봇·시스템학회 2021 International Journal of Control, Automation, and Vol.19 No.3
In this paper, an efficient and probabilistic complete planning algorithm called Composite-space RRT is presented to address motion planning with soft constraints for spherical wrist manipulators. Firstly, we propose a novel configuration space termed Composite Configuration Space (“Composite Space” for short), which is composed of the joint space and the task space. Then, collision-free paths are generated in the composite space by the Rapidly-exploring Random Trees (RRT) algorithm. Finally, the planned paths in the composite space are mapped into the corresponding joint-space paths by a local planner. As the analytical inverse kinematics (IK) of the spherical wrist is used in the local planner, the proposed Composite-space RRT algorithm is characterized by high efficiency and no numerical iteration. Moreover, this approach can effectively improve the smoothness of the end-effector orientation path. The effectiveness of the proposed algorithm is demonstrated on the Willow Garage’s PR2 simulation platform with two typical orientation-constrained cases.