Optimal Design and Redundancy Resolution of a Novel Robotic Two-Fingered Exoskeleton

Orlando, M. Felix; Behera, Laxmidhar; Dutta, Ashish; Saxena, Anupam

doi:10.1109/tmrb.2020.2970114

Cited by 15 publications

(7 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After comprehensive training of the KSOM network, the approximation error β is approximately close to zero. As a result, the proposed method's Lyapunov stability is guaranteed through accurate offline learning of the KSOM network [28].…”

Section: Stability Analysis Of the Proposed Methodsmentioning

confidence: 95%

“…Each neuron is associated with a weight vector w n , which discretizes the input space, and a joint angle vector θ n , which discretizes the output space . A n is a proportional linear map or an approximated inverse Jacobian mapping between the input and output spaces [14,28]. The suggested system's 3-D KSOM lattice structure is shown in Fig.…”

Section: Controller Modelmentioning

confidence: 99%

“…The initial phase of KSOM exhibits a significant positioning error compared to the second phase due to its open-loop mode, where only the desired position is considered while ignoring the current position [28]. This problem is solved by using fine movement, which reduces the error between the target and actual position.…”

Section: Jacobian Inverse Approximationmentioning

confidence: 99%

See 2 more Smart Citations

Redundancy Resolution of a Mobile Manipulator Using Learning Based Algorithm

Tolossa,

Gupta,

Orlando

et al. 2024

Preprint

View full text Add to dashboard Cite

A learning-based strategy for the trajectory tracking of redundant mobile manipulators (MM) was presented in this study. A five-degrees-of-freedom (DOF) manipulator is mounted on the differential drive (DD) mobile robot. The advantage of using a redundant system is to avoid joint limits, obstacles and singularities towards desired trajectory tracking. The proposed approach is based on Kohonen Self-Organizing Map (KSOM) enhanced a weighted least norm (WLN) matrix algorithm. This approach is the recommended neural network for inverse kinemat-ics solutions because of its stability, preserved topology, and capacity to optimize the joint space trajectory while producing a smooth minimal joint angle. A proposed method for redundancy resolution in MM has been simulated using MATLAB simulation code and the Gazebo real-time simulation pysical environment. The simulation results are evaluated with the joint limit method of redundancy resolution and other existing controllers for verification purpose. The conventional method of redundancy resolution is local optimum and infeasible for the end-effector motion in the entire workspace. The KSOM uses different steps of error correction that improve the system’s performance as well as ensure the global asymptotical stability of the system.

show abstract

Section: Stability Analysis Of the Proposed Methodsmentioning

confidence: 95%

Section: Controller Modelmentioning

confidence: 99%

See 1 more Smart Citation

Redundancy Resolution of a Mobile Manipulator Using Learning Based Algorithm

Tolossa,

Gupta,

Orlando

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Compared with soft exoskeletons, this design involves linkages that are bulky and rigid, which potentially provides an uncomfortable wear experience and a heavy burden for the user [ 48 , 49 , 50 ]. Furthermore, misalignment of the finger joint (axis) and exoskeleton joint (axis) is commonly found in current designs, which potentially leads to discomfort and skin abrasion [ 36 , 51 , 52 ]. However, the linkage-driven mechanism is still widely adopted in hand exoskeletons due to the large force transmission efficiency, precise joint trajectory control potential, and reliability of the mechanism [ 53 ].…”

Section: Introductionmentioning

confidence: 99%

Hand Exoskeleton Design and Human–Machine Interaction Strategies for Rehabilitation

et al. 2022

View full text Add to dashboard Cite

Stroke and related complications such as hemiplegia and disability create huge burdens for human society in the 21st century, which leads to a great need for rehabilitation and daily life assistance. To address this issue, continuous efforts are devoted in human–machine interaction (HMI) technology, which aims to capture and recognize users’ intentions and fulfil their needs via physical response. Based on the physiological structure of the human hand, a dimension-adjustable linkage-driven hand exoskeleton with 10 active degrees of freedom (DoFs) and 3 passive DoFs is proposed in this study, which grants high-level synergy with the human hand. Considering the weight of the adopted linkage design, the hand exoskeleton can be mounted on the existing up-limb exoskeleton system, which greatly diminishes the burden for users. Three rehabilitation/daily life assistance modes are developed (namely, robot-in-charge, therapist-in-charge, and patient-in-charge modes) to meet specific personal needs. To realize HMI, a thin-film force sensor matrix and Inertial Measurement Units (IMUs) are installed in both the hand exoskeleton and the corresponding controller. Outstanding sensor–machine synergy is confirmed by trigger rate evaluation, Kernel Density Estimation (KDE), and a confusion matrix. To recognize user intention, a genetic algorithm (GA) is applied to search for the optimal hyperparameters of a 1D Convolutional Neural Network (CNN), and the average intention-recognition accuracy for the eight actions/gestures examined reaches 97.1% (based on K-fold cross-validation). The hand exoskeleton system provides the possibility for people with limited exercise ability to conduct self-rehabilitation and complex daily activities.

show abstract

“…Force-based control is one of the control strategies for finger rehabilitation robots [28]. Cheng et al [29] proposed a controller combing the iterative learning control (ILC) and the active disturbance rejection control (ADRC) to adapt the repeating training manner and overcome the external interference in a wearable hand rehabilitation robot.…”

Section: Introductionmentioning

confidence: 99%

Mechanical Design and Analysis of the End-Effector Finger Rehabilitation Robot (EFRR) for Stroke Patients

et al. 2021

View full text Add to dashboard Cite

Most existing finger rehabilitation robots are structurally complex and cannot be adapted to multiple work conditions, such as clinical and home. In addition, there is a lack of attention to active adduction/abduction (A/A) movement, which prevents stroke patients from opening the joint in time and affects the rehabilitation process. In this paper, an end-effector finger rehabilitation robot (EFRR) with active A/A motion that can be applied to a variety of applications is proposed. First, the natural movement curve of the finger is analyzed, which is the basis of the mechanism design. Based on the working principle of the cam mechanism, the flexion/extension (F/E) movement module is designed and the details used to ensure the safety and reliability of the device are introduced. Then, a novel A/A movement module is proposed, using the components that can easily individualized design to achieve active A/A motion only by one single motor, which makes up for the shortcomings of the existing devices. As for the control system, a fuzzy proportional-derivative (PD) adaptive impedance control strategy based on the position information is proposed, which can make the device more compliant, avoid secondary injuries caused by excessive muscle tension, and protect the fingers effectively. Finally, some preliminary experiments of the prototype are reported, and the results shows that the EFRR has good performance, which lays the foundation for future work.

show abstract

Optimal Design and Redundancy Resolution of a Novel Robotic Two-Fingered Exoskeleton

Cited by 15 publications

References 31 publications

Redundancy Resolution of a Mobile Manipulator Using Learning Based Algorithm

Redundancy Resolution of a Mobile Manipulator Using Learning Based Algorithm

Hand Exoskeleton Design and Human–Machine Interaction Strategies for Rehabilitation

Mechanical Design and Analysis of the End-Effector Finger Rehabilitation Robot (EFRR) for Stroke Patients

Contact Info

Product

Resources

About