Xie, Biyun

Project description:

The human arm can be modeled as a kinematic chain with seven degrees of freedom, three rotational joints at the shoulder, one rotational joint at the elbow, and three rotational joints at the wrist. Human arm motions can be roughly divided into two categories, i.e., reaching movements and grasping movements. In both reaching and grasping movements, the human arm has one degree of redundancy (the difference between the DOFs of a robot and its workspace dimension), which can be physically interpreted as there are infinitely many arm postures at one target location. The specific scientific objectives of this research are: (1) analyze natural human arm postures in reaching movements and develop a method to predict corresponding natural human arm postures. (2) Analyze natural human arm postures in grasping movements and develop a method to predict corresponding natural human arm postures. (3) Based on these synthesized human arm postures, develop an inverse kinematics algorithm to generate human-like motions for collaborative robots in different tasks.

Personnel:

• Biyun Xie (PI)
• Landon Clarks (Ph.D. student), Added 06/10/2021, Added to MCC cluster on 07/12/2023 
• Huitao Guan (Ph.D. student), Added 06/10/2021
• Josh Ashley (master student), Added 06/10/2021
• Daniel Kennedy (undergraduate student), Added 06/10/2021
  

Method:

• Deep learning (in development)

Software:

• MATLAB (available at UK)
• Python (free access online)

UK and non-UK collaborators:

None

Grants:

Publications: