Motion Planning of Upper-Limb Exoskeleton Robots: A Review

Bio-Inspired Motion Emulation for Social Robots: A Real-Time Trajectory Generation and Control Approach

Assistive robotic platforms have recently gained popularity in various healthcare applications, and their use has expanded to social settings such as education, tourism, and manufacturing. These social robots, often in the form of bio-inspired humanoid systems, provide significant psychological and physiological benefits through one-on-one interactions. To optimize the interaction between social robotic platforms and humans, it is crucial for these robots to identify and mimic human motions in real time. This research presents a motion prediction model developed using convolutional neural networks (CNNs) to efficiently determine the type of motions at the initial state. Once identified, the corresponding reactions of the robots are executed by moving their joints along specific trajectories derived through temporal alignment and stored in a pre-selected motion library. In this study, we developed a multi-axial robotic arm integrated with a motion identification model to interact with humans by emulating their movements. The robotic arm follows pre-selected trajectories for corresponding interactions, which are generated based on identified human motions. To address the nonlinearities and cross-coupled dynamics of the robotic system, we applied a control strategy for precise motion tracking. This integrated system ensures that the robotic arm can achieve adequate controlled outcomes, thus validating the feasibility of such an interactive robotic system in providing effective bio-inspired motion emulation.

Shaping high-performance wearable robots for human motor and sensory reconstruction and enhancement

Most wearable robots such as exoskeletons and prostheses can operate with dexterity, while wearers do not perceive them as part of their bodies. In this perspective, we contend that integrating environmental, physiological, and physical information through multi-modal fusion, incorporating human-in-the-loop control, utilizing neuromuscular interface, employing flexible electronics, and acquiring and processing human-robot information with biomechatronic chips, should all be leveraged towards building the next generation of wearable robots. These technologies could improve the embodiment of wearable robots. With optimizations in mechanical structure and clinical training, the next generation of wearable robots should better facilitate human motor and sensory reconstruction and enhancement.

Gait-Oriented Post-Stroke Rehabilitation Tasks Online Trajectory Generation for 1-DOF Hip Lower-Limb Exoskeleton

In the field of gait rehabilitation lower limb exoskeletons have received a lot of interest. An increasing number of them are revised to be adapted for post-stroke rehabilitation. These exoskeletons mostly work in complement of conventional physiotherapy in the subacute phase to practice gait training. For this gait training the reference trajectory generation is one of the main issues. This is why it usually consists in reproducing some averaged healthy patient's gait pattern. This paper's purpose is to display the online trajectory generation (OTG) algorithm developed to provide reference trajectories applied to gait-oriented tasks designed based on conventional physiotherapy. This OTG algorithm is made to reproduce trajectories similar to the ones a therapist would follow during the same tasks. In addition, experiments are presented in this paper to compare the trajectories generated with the OTG algorithm for two rehabilitation tasks with the trajectories followed by a therapist in the same conditions. During these experiments the OTG is implemented in a runtime system with a 500µs cycle time on a bench able to emulate late and early patients' interaction. These experiments results assess that the OTG can work at a 500µs cycle time to reproduce a similar trajectory as the one followed by the therapist during the two rehabilitation tasks implemented.

Isokinetic Rehabilitation Trajectory Planning of an Upper Extremity Exoskeleton Rehabilitation Robot Based on a Multistrategy Improved Whale Optimization Algorithm

Upper extremity exoskeleton rehabilitation robots have become a significant piece of rehabilitation equipment, and planning their motion trajectories is essential in patient rehabilitation. In this paper, a multistrategy improved whale optimization algorithm (MWOA) is proposed for trajectory planning of upper extremity exoskeleton rehabilitation robots with emphasis on isokinetic rehabilitation. First, a piecewise polynomial was used to construct a rough trajectory. To make the trajectory conform to human-like movement, a whale optimization algorithm (WOA) was employed to generate a bounded jerk trajectory with the minimum running time as the objective. The search performance of the WOA under complex constraints, including the search capability of trajectory planning symmetry, was improved by the following strategies: a dual-population search, including a new communication mechanism to prevent falling into the local optimum; a mutation centroid opposition-based learning, to improve the diversity of the population; and an adaptive inertia weight, to balance exploration and exploitation. Simulation analysis showed that the MWOA generated a trajectory with a shorter run-time and better symmetry and robustness than the WOA. Finally, a pilot rehabilitation session on a healthy volunteer using an upper extremity exoskeleton rehabilitation robot was completed safely and smoothly along the trajectory planned by the MWOA. The proposed algorithm thus provides a feasible scheme for isokinetic rehabilitation trajectory planning of upper extremity exoskeleton rehabilitation robots.

Anthropomorphic Reaching Movement Generating Method for Human-Like Upper Limb Robot

How to generate anthropomorphic reaching movement remains a challenging problem in service robots and human motor function repair/reconstruction equipment. However, there is no universally accepted computational model in the literature for reproducing the motion of the human upper limb. In response to the problem, this article presents a computational framework for generating reaching movement endowed with human motion characteristics that imitated the mechanism in the control and realization of human upper limb motions. This article first establishes the experimental paradigm of human upper limb functional movements and proposes the characterization of human upper limb movement characteristics and feature movement clustering methods in the joint space. Then, according to the specific task requirements of the upper limb, combined with the human sensorimotor model, the estimation method of the human upper limb natural postures was established. Next, a continuous task parametric model matching the characteristic motion class is established by using the Gaussian mixture regression method. The anthropomorphic motion generation method with the characteristics of the smooth trajectory and the ability of natural obstacle avoidance is proposed. Finally, the anthropomorphic motion generation method proposed in this article is verified by a human-like robot. The measurement index of the human-likeness degree of the trajectory is given. The experimental results show that for all four tested tasks, the human-likeness degrees were greater than 90.8%, and the trajectories' jerk generated by this method is very similar to the trajectories' jerk of humans, which validates the proposed method.

Developing an Upper Limb Kinematics Database of Activities of Daily Living

Open-access databases can facilitate data sharing among researchers and provide normative data for objective clinical assessment development, robotic design, and biomechanical modeling. However, most existing databases focus on gait, balance, and hand gestures without providing elbow and shoulder kinematics that are required in activities of daily living. Furthermore, the few existing upper limb datasets include small sample sizes without consistent data collection protocols, which hinder robotic engineers' ability to design robotic devices that accommodate the general population. To address the literature gap, an open-access upper limb kinematic database was proposed. Due to the impact of COVID-19 on human research, only data from 16 participants were collected. Clinical Relevance-This provides baseline kinematics for developing objective clinical assessments and rehabilitation robots.

Industrial exoskeletons for overhead work: Circumferential pressures on the upper arm caused by the physical human-machine-interface

This study investigated the pressures occurring within the arm human-machine-interfaces (HMI) of four different exoskeletons that support static and dynamic work at or above head level, and the effects of the HMI on neurovascular supply of the upper extremity using an orthopedic provocation maneuver with raised arms with and without the exoskeletons. Decreased time in the provocation maneuver with exoskeletons indicated a negative effect of the HMIs on the vascular and neural supply of the arm. Average pressure in the static situation was 3.2 ± 0.7 kPa and 4.4 ± 0.4 kPa with regular peak values of 6.5 ± 0.5 kPa in the dynamic task. These pressures were significantly higher than the pressure values that guarantee adequate tissue oxygenation. It remains unknown whether the way exoskeletons apply pressure affects vascular and neural supply to the arms, or whether the regular unloading during dynamic activity has a neutralizing effect.

Active Disturbance Rejection Control Based Sinusoidal Trajectory Tracking for an Upper Limb Robotic Rehabilitation Exoskeleton

In this paper, a combined control strategy with extended state observer (ESO) and finite time stable tracking differentiator (FTSTD) has been proposed to perform flexion and extension motion repetitively and accurately in the sagittal plane for shoulder and elbow joints. The proposed controller improves the tracking accuracy, performs state estimation, and actively rejects disturbance. A sinusoidal trajectory as an input has been given to a two-link multiple-input multiple-output (MIMO) upper limb robotic rehabilitation exoskeleton (ULRRE) for a passive rehabilitation purpose. The efficacy of the controller has been tested with the help of performance indices such as integral time square error (ITSE), integral square error (ISE), integral time absolute error (ITAE), and integral of the absolute magnitude of error (IAE). The system model is obtained through the Euler–Lagrangian method, and the controller’s stability is also given. The proposed controller has been simulated for ±20% parameter variation with constant external disturbances to test the disturbance rejection ability and robustness against parametric uncertainties. The proposed controller has been compared with already developed ESO-based methods such as active disturbance rejection control (ADRC), nonlinear active disturbance rejection control (NLADRC), and improved active disturbance rejection control (I-ADRC). It has been found that the proposed method increases tracking performance, as evidenced by the above performance indices.

Kinematic of the Position and Orientation Synchronization of the Posture of a n DoF Upper-Limb Exoskeleton with a Virtual Object in an Immersive Virtual Reality Environment

Exoskeletons are an external structural mechanism with joints and links that work in tandem with the user, which increases, reinforces, or restores human performance. Virtual Reality can be used to produce environments, in which the intensity of practice and feedback on performance can be manipulated to provide tailored motor training. Will it be possible to combine both technologies and have them synchronized to reach better performance? This paper consists of the kinematics analysis for the position and orientation synchronization between an n DoF upper-limb exoskeleton pose and a projected object in an immersive virtual reality environment using a VR headset. To achieve this goal, the exoskeletal mechanism is analyzed using Euler angles and the Pieper technique to obtain the equations that lead to its orientation, forward, and inverse kinematic models. This paper extends the author’s previous work by using an early stage upper-limb exoskeleton prototype for the synchronization process.