Motion Imitation and Augmentation System for a Six Degrees of Freedom Dual-Arm Robot

Abstract

A motion imitation and augmentation system comprising an imitation system, a self-collision avoidance system, and a motion augmented system was proposed in this study. The imitation system captures the human skeleton by using an RGB-D camera and a method involving forward kinematics and the space vector method was developed and integrated to map the human joint angles to the motor angles of the robot's arm. To improve safety of the system, a self-collision avoidance system was incorporated so that the various parts of the robot do not collide with each other while imitating human motions. The imitated motion was recorded and stored in a motion dataset as a reference motion. To address different situations and objects, the reference motion was adjusted using the motion augmented system in which both the adjusted trajectory and its corresponding motor angles were generated using the integrated adaptive constricted particle swarm optimization algorithm. So that, the adjusted motion can adapt to the real situation while maintaining similarity with the reference motion. Experiments conducted in practical situations demonstrated the efficiency of the motion imitation and augmentation system. The robot can mimic a reference motion by using its vision and can generate a suitable motion based on the imitated motion for coping with different objects and situations.

Fuzzy Feedback Control for Electro-Hydraulic Actuators

“…where C d is valve coefficient Similarly, in case of the right pilot, the operated check valve (v 2 ) [10]:…”

“…This system has demonstrated that robotic arms with the mini-EHA system can achieve higher power than their weight. In general, robotic arms with small size and light load capacity can use electric actuators [9,10]. In contrast, EHA is used to develop many heavy-duty applications because it can carry heavy loads with high accuracy and low cost.…”

Robust real-time upper limb motion transfer from human to humanoid for manipulation skill teaching

“…Therefore, the goal is to obtain the joint velocity _ u satisfying the Problem ( 9)-( 13) in real time. It is notable that equation (10) limits the motion of the full arm, while equation (11) imposes additional restrictions on the first k joints. Equations ( 12) and ( 13) then jointly limit the range of decision variables.…”

Learning to teleoperate an upper-limb assistive humanoid robot for bimanual daily-living tasks

“…Although different objects and instructions were obviously used, the bimanual capability and the intuitive control of our setup would, in all likelihood, be the reason behind the shorter times obtained in our experiment. While bimanual teleoperation has been widely studied [34,35,36,37,38,39], the tracking method used is often wired or dependent on external tracking [40,41], including also our previously published video 2 [42], which has initiated this study. In this work, we use a wearable, wireless and independent device both for hand movement recognition and upper-body tracking.…”

“…Although bimanual teleoperation has been widely studied [38][39][40][41][42][43], our work was mainly evaluating home tasks, while other bimanual teleoperation experiments have focused on field tasks where TCTs, difficulty and success rates were also evaluated [44]. The tracking method used is often wired or dependent on external tracking [45,46], including also our previously published video 2 [47], which has initiated this study (of which initial results were presented in [48]). In this work, we use a wearable, wireless and independent device both for hand movement recognition and upper-body tracking.…”