Robotic arm control through human arm movement detection using potentiometers

Author(s):  
Edwin Basil Mathew ◽  
Dushyant Khanduja ◽  
Bhavya Sapra ◽  
Bharat Bhushan
Author(s):  
Sherrie Holder ◽  
Leia Stirling

There are many robotic scenarios that require real-time function in large or unconstrained environments, for example, the robotic arm on the International Space Station (ISS). Use of fully-wearable gesture control systems are well-suited to human-robot interaction scenarios where users are mobile and must have hands free. A human study examined operation of a simulated ISS robotic arm using three different gesture input mappings compared to the traditional joystick interface. Two gesture mappings permitted multiple simultaneous inputs (multi-input), while the third was a single-input method. Experimental results support performance advantages of multi-input gesture methods over single input. Differences between the two multi-input methods in task completion and workload display an effect of user-directed attention on interface success. Mappings based on natural human arm movement are promising for gesture interfaces in mobile robotic applications. This study also highlights challenges in gesture mapping, including how users align gestures with their body and environment.


2020 ◽  
Vol 17 (5) ◽  
pp. 172988142093757
Author(s):  
Lina Hao ◽  
Zhirui Zhao ◽  
Xing Li ◽  
Mingfang Liu ◽  
Hui Yang ◽  
...  

Manual lifting tasks involve repetitive raising, holding and stacking movements with heavy objects. These arm movements are notable risk factors for muscle pain, fatigue, and musculoskeletal disorders in workers. An upper-limb wearable robot, as a 6-DOF dual-arm exoskeleton, which was designed to augment workers’ strength and minimize muscular activation in the arm during repetitive lifting tasks. To adjust the robot joint trajectory, the user needs to apply an interactive torque to operate the robot during lifting tasks when a standard virtual mechanical impedance control structure is used. To reduce overshooting of the interactive torque on the user’s joint, a three-tier hierarchical control structure was developed for the robot in this study. At the highest level, a human arm movement detection module is used to detect the user’s arm motion according to the surface electromyography signals. Then, a Hammerstein adaptive virtual mechanical impedance controller is used at the middle level to reduce overshooting and yield an acceptable value of torque for the user’s elbow joint in actual lifting tasks. At the lowest level, the actuator controller on each joint of the robot controls the robot to complete lifting tasks. Several experiments were conducted, and the results showed that the interactive torque on the user’s elbow was limited and the muscular activations of erector spinae and biceps brachii muscles were effectively decreased. The proposed scheme prevents potential harm to the user due to excessive interactive torque on the human elbow joint, such as related muscle fatigue and joint injuries.


2021 ◽  
Author(s):  
Asif Arefeen ◽  
Yujiang Xiang

Abstract In this paper, an optimization-based dynamic modeling method is used for human-robot lifting motion prediction. The three-dimensional (3D) human arm model has 13 degrees of freedom (DOFs) and the 3D robotic arm (Sawyer robotic arm) has 10 DOFs. The human arm and robotic arm are built in Denavit-Hartenberg (DH) representation. In addition, the 3D box is modeled as a floating-base rigid body with 6 global DOFs. The interactions between human arm and box, and robot and box are modeled as a set of grasping forces which are treated as unknowns (design variables) in the optimization formulation. The inverse dynamic optimization is used to simulate the lifting motion where the summation of joint torque squares of human arm is minimized subjected to physical and task constraints. The design variables are control points of cubic B-splines of joint angle profiles of the human arm, robotic arm, and box, and the box grasping forces at each time point. A numerical example is simulated for huma-robot lifting with a 10 Kg box. The human and robotic arms’ joint angle, joint torque, and grasping force profiles are reported. These optimal outputs can be used as references to control the human-robot collaborative lifting task.


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