Lower limb exoskeleton robots’ dynamics parameters identification based on improved beetle swarm optimization algorithm

Beetle Antennae Search (BAS) is an intelligent algorithm that mimics the beetle preying process. In BAS, there is a beetle which has a left and a right antennae sensing the intensity of the odor emitted by the food. The beetle learns the different sense of two antennas to determine the flight and finally finds the food. The BAS algorithm has been proved to have good optimizing speed and precision when it is applied for low-dimensional optimization problems. However, when solving high-dimensional problems, the algorithm is easily trapped into local optimum. In order to improve the optimization ability of BAS, we have combined particle swarm optimization (PSO) algorithm and proposed a new hybrid BAS and PSO algorithm (BAS-PSO). In BAS-PSO, firstly, the standard PSO is used for particle velocity and position updating. Particles learn ontology information and group optimal information for evolution. Then each particle in the swarm is taken as a beetle independently applying a local search with BAS algorithm. To verify the performance of BAS-PSO, four benchmark functions have been selected in our simulation experiments. The results show that the performance of BAS-PSO is better than BAS and PSO.

The lower limb exoskeleton robot is a new technique of rehabilitation training for lower limb dysfunction, which is paid more and more attention by people. Because the lower limb exoskeleton robot is relatively heavy all over the world, this article designs a new lower limb exoskeleton robot whose hip joint is driven by motor and other joints are unpowered. It effectively reduces the lower limb exoskeleton robot’s weight. The kinematics equation of exoskeleton robot is established by D-H coordinate system, and the correctness of kinematics equation is verified with combining simulation by MATLAB and SolidWorks software. The end coordinate data calculated by kinematics equation is used as a contrast to test, and a good walking effect is achieved.

Stroke is a significant cause of disability worldwide, and stroke survivors often experience severe motor impairments. Lower limb rehabilitation exoskeleton robots provide support and balance for stroke survivors and assist them in performing rehabilitation training tasks, which can effectively improve their quality of life during the later stages of stroke recovery. Lower limb rehabilitation exoskeleton robots have become a hot topic in rehabilitation therapy research. This review introduces traditional rehabilitation assessment methods, explores the possibility of lower limb exoskeleton robots combining sensors and electrophysiological signals to assess stroke survivors' rehabilitation objectively, summarizes standard human-robot coupling models of lower limb rehabilitation exoskeleton robots in recent years, and critically introduces adaptive control models based on motion intent recognition for lower limb exoskeleton robots. This provides new design ideas for the future combination of lower limb rehabilitation exoskeleton robots with rehabilitation assessment, motion assistance, rehabilitation treatment, and adaptive control, making the rehabilitation assessment process more objective and addressing the shortage of rehabilitation therapists to some extent. Finally, the article discusses the current limitations of adaptive control of lower limb rehabilitation exoskeleton robots for stroke survivors and proposes new research directions.

<title>Abstract</title> Image enhancement is a crucial process in various fields, including medical imaging, remote sensing, and digital photography. This study explores the application of the Beetle Antennae Search (BAS) algorithm for optimizing image enhancement parameters. The BAS algorithm is compared with the Particle Swarm Optimization (PSO) algorithm to evaluate its effectiveness in improving image quality. The objective is to enhance grayscale images by adjusting parameters such as A, B, C, and K to maximize a fitness function that incorporates edge information and entropy. Experimental results demonstrate that BAS consistently outperforms PSO in terms of convergence speed and image quality metrics, including Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). BAS-enhanced images exhibit significant improvements in contrast, brightness, and sharpness, making it a promising tool for image enhancement tasks.

Dynamic models play a critical role in the design of model-based controllers, which has a significant effect on the dynamic characteristics of motion equipment. This paper mainly focuses on the dynamic modeling and parameter identification for a gantry-type automated fiber placement (AFP) machine. First, a dynamic modeling process combining prismatic axes and revolute axes is conducted by Newton-Euler method, in which the effects of friction and hydraulic balance system are also considered. Then, as the convenience for parameter identification and the application in linearity control, the methods of dynamic model linearization and determination of minimum inertial parameters based on the multi-body system (MBS) theory are proposed, with which a dynamic model in the form of linearized minimum inertial parameters is consequently established. To identify the parameters in the model, key issues regarding excitation trajectory, filtering, and identification algorithm are discussed in detail. Finally, corresponding experiments are performed on the AFP machine, and experimental results show that there is a good agreement between the prediction of the model and the measurement in actuality. Data analysis shows that except for Z-axis, the relative error rates of the others are not greater than 5%, which proves the effectiveness of the established dynamic model and the identified parameters.

Dual Beetle Antennae Search system for optimal planning and robust control of 5-link biped robots

Active power-assist lower limb exoskeleton robots aim to enhance wearer assistance while ensuring wearer comfort and simplifying the exoskeleton’s design and control. This study proposes an active assistance method known as Equivalent Force on Connection (EFOC). The EFOC method effectively addresses the limitations encountered in conventional Joint Torque Proportional Compensation (JTPC) approaches. These limitations include the necessity for exoskeleton robot configurations to align with human limb structures for parallel assistance at each lower limb joint, as well as the exoskeleton’s inability to contribute a greater proportion of assistance due to the excessive load on specific skeletal and muscular structures, resulting in wearer discomfort. Furthermore, the effectiveness of the EFOC method is evaluated and validated for assistance during both the stance and swing phases of single-leg movements. Finally, the proposed EFOC method is implemented on a hydraulic-driven lower limb exoskeleton robot to assist wearers in squatting, stepping, and jumping locomotion. The experimental results demonstrate that the proposed EFOC method can effectively achieve the desired assistance effect.

Lower limb exoskeleton robot is a robot that functions as a walking tool of movement for the lower of the human body (usually made for patients with stroke and paraplegic). However, as time goes by lower limb exoskeleton robot is no longer reserved for patients with stroke and paraplegic, some of them are made to military requirements or the need to lift heavy stuff. In this case, the lower limb exoskeleton robot is used for rehabilitation aids for people with paraplegic who suffered an accident on the lower part of the human body (lower limb). This paper presents the design and simulation of the lower limb exoskeleton robot using CAD software to design, and Matlab/Simulink for simulation. The first step after the design of the lower limb exoskeleton robot is completed, we export the file of robot design to the SimMechanics (using first generation). After we obtain the results of the export file, we give a Proportional-Integral-Derivative (PID) controller at each revolute joint. Then, we use a Matlab/Simulink to analyze the response system of PID controller. For the stability analysis of controller, we plot the transfer function of system into the root locus method. Based on the results of simulation, the PID controller has been able to making each revolute joint of the lower limb exoskeleton robot to become stable.

The accurate identification and characterization of the accelerometer dynamic model parameters play an important role in improving the dynamic performance of the device or system with an accelerometer. To overcome the problem that the traditional single degree of freedom (SDF) dynamic model of the accelerometer cannot describe the dynamic characteristics beyond the first resonant frequency of the accelerometer, a two degree of freedom (TDF) dynamic model of the accelerometer was constructed. On this basis, a parameter identification method for the TDF dynamic model of the accelerometer based on the feature points coordinate estimation and amplitude correction was proposed. First, the zero frequency point coordinates of the accelerometer frequency response were obtained by the Hv method. The first and second resonance point coordinates were estimated by discrete spectrum correction and the least square (DSC-LS) method. Then, the amplitude correction coefficient was applied to eliminate the influence of series coupling on the amplitude. Finally, the TDF dynamic model parameters of the accelerometer were calculated through the feature point coordinates. The experimental results show that the method has high accuracy and can avoid the influence of series coupling on the parameter identification accuracy of the accelerometer’s TDF dynamic model without complex derivation and decoupling operations. The identified TDF dynamic model of the accelerometer can represent the dynamic characteristics with a higher frequency range.

A wearable lightweight lower-limb exoskeleton (LLEX) robot that can help paraplegic patients perform daily activities such as standing/sitting, walking, stair ascending/descending is proposed in this paper firstly. In order to ensure comfort between the exoskeleton and the wearer, a novel configuration of the exoskeleton robot that combines active and passive joints, adaptive structure size, and high compatibility with human physiological parameters is proposed. Secondly, this paper proposes a dynamic gait planning algorithm for stairs ascending and descending based on hip and ankle motion position planning, which is implemented to LLEX. It is intended to provide paraplegic patients with personalized gaits for independently ascending/descending stairs of diverse sizes with the help of LLEX. The system employs the inertial measurement unit (IMU) module located on the back to identify the wearer's movement intention while using the depth camera located on the front of the LLEX to detect heights and depths of different stairs for a dynamic gait. Experiments have been carried out to prove the feasibility and pervasiveness of the gait planning algorithm. The application of LLEX can help patients with various physiological characteristics to complete the task of ascending and descending stairs.

An exact classification of different gait phases is essential to enable the control of exoskeleton robots and detect the intentions of users. We propose a gait phase classification method based on neural networks using sensor signals from lower limb exoskeleton robots. In such robots, foot sensors with force sensing registers are commonly used to classify gait phases. We describe classifiers that use the orientation of each lower limb segment and the angular velocities of the joints to output the current gait phase. Experiments to obtain the input signals and desired outputs for the learning and validation process are conducted, and two neural network methods (a multilayer perceptron and nonlinear autoregressive with external inputs (NARX)) are used to develop an optimal classifier. Offline and online evaluations using four criteria are used to compare the performance of the classifiers. The proposed NARX-based method exhibits sufficiently good performance to replace foot sensors as a means of classifying gait phases.

Lower limb exoskeleton robots help with walking movements through mechanical force, by identifying the wearer’s walking intention. When the exoskeleton robot is lightweight and comfortable to wear, the stability of walking increases, and energy can be used efficiently. However, because it is difficult to implement the complex anatomical movements of the human body, most are designed simply. Due to this, misalignment between the human and robot movement causes the wearer to feel uncomfortable, and the stability of walking is reduced. In this paper, we developed a two degrees of freedom (2DoF) ankle exoskeleton robot with a subtalar joint and a talocrural joint, applying a four-bar linkage to realize the anatomical movement of a simple 1DoF structure mainly used for ankles. However, bidirectional tendon-driven actuators (BTDAs) do not consider the difference in a length change of both cables due to dorsiflexion (DF) and plantar flexion (PF) during walking, causing misalignment. To solve this problem, a BTDA was developed by considering the length change of both cables. Cable-driven actuators and exoskeleton robot systems create uncertainty. Accordingly, adaptive control was performed with a proportional-integral-differential neural network (PIDNN) controller to minimize system uncertainty.

A three-degree-of-freedom flight simulator platform with a translational and two rotational movement capabilities is studied. Based on the Lagrange method the dynamic model is established and then simplified by eliminating the sub-diagonal elements of the matrixes. A washout algorithm is employed to qualify the platform for flight simulation in limited space. In order to improve the modeling and experimental accuracies further when some dynamic parameters of an actual platform are unknown, the stepwise identification method is proposed to solve this problem. By dividing the driving torque into the load torque, reducer driving torque, screw driving toque and integrated friction torque, and main dynamic parameters are identified by applying proper trajectories and estimated by the weighed least square method. The procedures of identification are verified through comparing the driving forces from the identified model and those of servo motor encoders.

Research on robotic exoskeletons has rapidly expanded over the previous decade. Advances in robotic hardware and energy supplies have enabled viable prototypes for human testing. This review paper describes current lower limb robotic exoskeletons, with specific regard to common trends in the field. The preponderance of published literature lacks rigorous quantitative evaluations of exoskeleton performance, making it difficult to determine the disadvantages and drawbacks of many of the devices. We analyzed common approaches in exoskeleton design and the convergence, or lack thereof, with certain technologies. We focused on actuators, sensors, energy sources, materials, and control strategies. One of the largest hurdles to be overcome in exoskeleton research is the user interface and control. More intuitive and flexible user interfaces are needed to increase the success of robotic exoskeletons. In the last section, we discuss promising future solutions to the major hurdles in exoskeleton control. A number of emerging technologies could deliver substantial advantages to existing and future exoskeleton designs. We conclude with a listing of the advantages and disadvantages of the emerging technologies and discuss possible futures for the field.

Beetle antennae search algorithm (BAS) is based on the searching behavior of longhorn beetles. This newly proposed metaheuristic algorithm is used in the path and function synthesis of multi-bar mechanism. The optimization results are compared with that of other metaheuristic algorithms like genetic algorithm (GA), particle swarm optimization (PSO) and differential evolution (DE) et al. While BAS uses only one group of initial parameters to search the best result, the convergence and efficiency are tested with eight case studies results. Newly added parameter q increases the possibility to find better results during iterations and deals with a higher number of independent parameters efficiently. Revised BAS exhibits good performance both in path synthesis of four-bar mechanism with or without prescribed timing, while optimized parameters increases from 6 to 34, and extends to path and function combined synthesis of Stephenson III six-bar double dwell mechanism with 14 parameters.