Predicting Redundancy of a 7 DOF Upper Limb Exoskeleton Toward Improved Transparency between Human and Robot

Abstract

For a wearable robotic system which includes the same redundancy as the human arm, configuring the joint angles of the robotic arm in accordance with those of the operators arm is one of the crucial control mechanisms to minimize the energy exchange between human and robot. Thus it is important to understand the redundancy resolution mechanism of the human arm such that the inverse kinematics solution of these two coupled systems becomes identical. In this paper, the redundancy resolution of the human arm based on the wrist position and orientation is provided as a closed form solution for the practical robot control algorithm, which enables the robot to form the natural human arm configuration as the operator changes the position and orientation of the end effector. For this, the redundancy of the arm is expressed mathematically by defining the swivel angle. Then the swivel angle is expressed as a superposition of two components, which are reference swivel angle and the swivel angle offset, respectively. The reference swivel angle based on the wrist position is defined by the kinematic criterion that maximizes the manipulability along the vector connecting the wrist and the virtual target point on the head region as a cluster of important sensory organs. Then the wrist orientation change is mapped into a joint angle availablility function output and translated to the swivel angle offset with respect to the reference swivel angle. Based on the inverse kinematic formula the controller can transform the position and orientation of the endeffector into the joint torque which enables the robot to follow up the operator's current joint configuration. The estimation performance was evaluated by utilizing a motion capture system and the results show that there is a high correlation between the estimated and calculated swivel angles.

Mobile Mechatronic/Robotic Orthotic Devices to Assist–Rehabilitate Neuromotor Impairments in the Upper Limb: A Systematic and Synthetic Review

“…An important finding regards a quite promising and actual development trend: tracking for feedback and training arm (shoulder, elbow, forearm) and hand, together, produced “greater improvement” than such endeavors done separately for the respective anatomic regions (Merians et al, 2009 ). The methods to achieve that require the use of, including haptic-based, virtual reality (VR) facilities (Huang et al, 2012 ; Guo et al, 2013 ; Lin et al, 2013b ; Wei et al, 2013 ; Dowling et al, 2014 ; Song et al, 2014 ; Thielbar et al, 2014 ; Kim and Rosen, 2015 ; Shull and Damian, 2015 ; Grimm et al, 2016 ; Mazzoleni et al, 2017 ; Maris et al, 2018 ). This matches with the conceptual addition of, for instance, Brain Machine Interface (BMI) and/or neuromuscular electrical stimulation (NMES)/FES or transcranial Direct Current Stimulation (tDCS), respectively repetitive transcranial magnetic stimulation (rTMS), facilities usage, too.…”

“…Another quite recent-justified as being a key item that synthesizes, like a top of iceberg, many of the essential mechatronic determinants is the so-called “transparency” (Kim and Rosen, 2015 ; Proietti et al, 2016 ). This actually tends to improve the overall outcome, by being underpinned including on increased intuitiveness thus matching with the user's non invasively extracted EMG and/or EEG/BCI movement purposes (Onose et al, 2012 ).…”

“…The main pathology spectra in the upper limb, approachable by robotic devices evoked in the selected articles, consists of: stroke (the majority–41.66%) (Huang et al, 2012 ; Song et al, 2012 ; Kim et al, 2013 ; Wei et al, 2013 ; Xiao et al, 2014 ; Nycz et al, 2015 ; Kim and Rosen, 2015 ; Guo et al, 2016 ; Tu et al, 2017a , b ), traumatic brain injury (Giberti et al, 2014 ), spinal cord injury (Miller and Rosen, 2010 ; Frisoli et al, 2016 ), Parkinson's disease/tremor (Rocon et al, 2007 ; Nimawat and Jailiya, 2015 ; Shull and Damian, 2015 ; Freer et al, 2017 ), peripheral nerve lesions in the upper limb–including with carpal tunnel syndrome–(Noveanu et al, 2013 ; Giberti et al, 2014 ; Andrikopoulos et al, 2015 ; Shull and Damian, 2015 ; Frisoli et al, 2016 ). Aside those above mentioned, in the additional literature we have studied, there are to be found also the following indications: Cerebral Palsy, Multiple Sclerosis, Spinal Muscular Atrophy, Brachial Plexus Injury, Arthrogryposis Multiplex Congenita (Rahman et al, 2006 ; Haumont et al, 2011 ; Maciejasz et al, 2014 ; Lopez et al, 2014 ; Gilliaux et al, 2015 ).…”

Position-based kinematics for 7-DoF serial manipulators with global configuration control, joint limit and singularity avoidance

“…The works of Tondu [7], Asfour and Dillmann [20], Kim and Rosen [21] all report analytic inverse kinematic solutions, based on position-control. With small differences when estimating the arm angle, these works take advantage of the manipulator's redundancy to enhance the human-likeness of the movement.…”

“…Other methods to describe the self-motion were later introduced [7,8]. The redundant degree of freedom enables the manipulator to perform secondary tasks aside from reaching a specific position and orientation [9], such as: singularities [10,11,12] and joint limits avoidance [13,14,15], obstacle avoidance [16,17], minimise energy dissipation [18], enhance manipulability [19] and human-like motions [7,20,21].…”