Abstract
The proposal of postural synergy theory has provided a new approach to solve the problem of controlling anthropomorphic hands with multiple degrees of freedom. However, generating the grasp configuration for new tasks in this context remains challenging. This study proposes a method to learn grasp configuration according to the shape of the object by using postural synergy theory. By referring to past research, an experimental paradigm is first designed that enables the grasping of 50 typical objects in grasping and operational tasks. The angles of the finger joints of 10 subjects were then recorded when performing these tasks. Following this, four hand primitives were extracted by using principal component analysis, and a low-dimensional synergy subspace was established. The problem of planning the trajectories of the joints was thus transformed into that of determining the synergy input for trajectory planning in low-dimensional space. The average synergy inputs for the trajectories of each task were obtained through the Gaussian mixture regression, and several Gaussian processes were trained to infer the inputs trajectories of a given shape descriptor for similar tasks. Finally, the feasibility of the proposed method was verified by simulations involving the generation of grasp configurations for a prosthetic hand control. The error in the reconstructed posture was compared with those obtained by using postural synergies in past work. The results show that the proposed method can realize movements similar to those of the human hand during grasping actions, and its range of use can be extended from simple grasping tasks to complex operational tasks.
Highlights
Recent technological advances in robotics and related areas have led to the development of sophisticated anthropomorphic hands with an increasing number of degrees of freedom (DoFs) (Belter et al, 2013; Portnova-Fahreeva et al, 2020)
Several grasping modes are planned in advance, the operator’s intention is identified, and finite-state machines (FSM) or pattern recognition (PR) methods are used to switch between modes (Purushothaman, 2016)
Four hand primitives were extracted by principal component analysis (PCA), and the corresponding trajectories of the synergy inputs during each task were saved
Summary
Recent technological advances in robotics and related areas have led to the development of sophisticated anthropomorphic hands with an increasing number of degrees of freedom (DoFs) (Belter et al, 2013; Portnova-Fahreeva et al, 2020). Due to improvements in their flexibility, such anthropomorphic hands can perform tasks requiring dexterity in several areas of the manufacturing and services industries (Leidner et al, 2015). Several grasping modes are planned in advance, the operator’s intention is identified, and finite-state machines (FSM) or pattern recognition (PR) methods are used to switch between modes (Purushothaman, 2016). The disadvantages of this method are a slow controller and poor universality because different modes are discrete (Pylatiuk et al, 2007). It is important to find an appropriate control strategy to improve the operational capability and range of application scenarios of anthropomorphic hands
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