A Novel Cost Calculation Method for Manipulator Trajectory Planning.

It is worthwhile to calculate the execution cost of a manipulator for selecting a planning algorithm to generate trajectories, especially for an agricultural robot. Although there are various off-the-shelf trajectory planning methods, such as pursuing the shortest stroke or the smallest time cost, they often do not consider factors synthetically. This paper uses the state-of-the-art Python version of the Robotics Toolbox for manipulator trajectory planning instead of the traditional D-H method. We propose a cost function with mass, iteration, and residual to assess the effort of a manipulator. We realized three inverse kinematics methods (NR, GN, and LM with variants) and verified our cost function's feasibility and effectiveness. Furthermore, we compared it with state-of-the-art methods such as Double A* and MoveIt. Results show that our method is valid and stable. Moreover, we applied LM (Chan λ = 0.1) in mobile operation on our agricultural robot platform.

Multibody Model with Foot-Deformation Approach for Estimating Ground Reaction Forces and Moments and Joint Torques during Level Walking through Optical Motion Capture without Optimization Techniques.

The biomechanical-model-based approach with a contact model offers advantages in estimating ground reaction forces (GRFs) and ground reaction moments (GRMs), as it does not rely on the need for training data and gait assumptions. However, this approach faces the challenge of long computational times due to the inclusion of optimization processes. To address this challenge, the present study developed a new optical motion capture (OMC)-based method to estimate GRFs, GRMs, and joint torques without prolonged computational times. The proposed approach performs the estimation process by distributing external forces, as determined by a multibody model, between the left and right feet based on foot deformations, thereby predicting the GRFs and GRMs without relying on optimization techniques. In this study, prediction accuracies during level walking were confirmed by comparing a general analysis using a force plate with the estimation results. The comparison revealed excellent or strong correlations between the prediction and the measurements for all GRFs, GRMs, and lower-limb-joint torques. The proposed method, which provides practical estimation with low computational cost, facilitates efficient biomechanical analysis and rapid feedback of analysis results, contributing to its increased applicability in clinical settings.

Inclusion of a skeletal model partly improves the reliability of lower limb joint angles derived from a markerless depth camera.

A single depth camera provides a fast and easy approach to performing biomechanical assessments in a clinical setting; however, there are currently no established methods to reliably determine joint angles from these devices. The primary aim of this study was to compare joint angles as well as the between-day reliability of direct kinematics to model-constrained inverse kinematics recorded using a single markerless depth camera during a range of clinical and athletic movement assessments.A secondary aim was to determine the minimum number of trials required to maximize reliability. Eighteen healthy participants attended two testing sessions one week apart. Tasks included treadmill walking, treadmill running, single-leg squats, single-leg countermovement jumps, bilateral countermovement jumps, and drop vertical jumps. Keypoint data were processed using direct kinematics as well as in OpenSim using a full-body musculoskeletal model and inverse kinematics. Kinematic methods were compared using statistical parametric mapping and between-day reliability was calculated using intraclass correlation coefficients, mean absolute error, and minimal detectable change. Keypoint-derived inverse kinematics resulted in significantly smaller hip flexion (range = -9 to -2°), hip abduction (range = -3 to -2°), knee flexion (range = -5° to -2°), and greater dorsiflexion angles (range = 6-15°) than direct kinematics. Both markerless kinematic methods had high between-day reliability (inverse kinematics ICC 95 %CI = 0.83-0.90; direct kinematics ICC 95 %CI = 0.80-0.93). For certain tasks and joints, keypoint-derived inverse kinematics resulted in greater reliability (up to 0.47 ICC) and smaller minimal detectable changes (up to 13°) than direct kinematics. Performing 2-4 trials was sufficient to maximize reliability for most tasks. A single markerless depth camera can reliably measure lower limb joint angles, and skeletal model-constrained inverse kinematics improves lower limb joint angle reliability for certain tasks and joints.

Human arm redundancy: a new approach for the inverse kinematics problem.

The inverse kinematics (IK) problem addresses how both humans and robotic systems coordinate movement to resolve redundancy, as in the case of arm reaching where more degrees of freedom are available at the joint versus hand level. This work focuses on which coordinate frames best represent human movements, enabling the motor system to solve the IK problem in the presence of kinematic redundancies. We used a multi-dimensional sparse source separation method to derive sets of basis (or source) functions for both the task and joint spaces, with joint space represented by either absolute or anatomical joint angles. We assessed the similarities between joint and task sources in each of these joint representations, finding that the time-dependent profiles of the absolute reference frame's sources show greater similarity to corresponding sources in the task space. This result was found to be statistically significant. Our analysis suggests that the nervous system represents multi-joint arm movements using a limited number of basis functions, allowing for simple transformations between task and joint spaces. Additionally, joint space seems to be represented in an absolute reference frame to simplify the IK transformations, given redundancies. Further studies will assess this finding's generalizability and implications for neural control of movement.

Bipedal Robot Gait Generation Using Bessel Interpolation.

This paper introduces a novel approach to bipedal robot gait generation by proposing a higher-order form through the parameter equation of first-order Bessel interpolation. The trajectory planning for the bipedal robot, specifically for stepping up or down stairs, is established based on a three-dimensional interpolation equation. The experimental prototype, Roban, is utilized for the study, and the structural sketch of a single leg is presented. The inverse kinematics expression for the leg is derived using kinematic methods. Employing a position control method, the angle information is transmitted to the robot's joints, enabling the completion of both downstairs simulation experiments and physical experiments with the Roban prototype. The analysis of the experimental process reveals a noticeable phenomenon of hip and ankle joint tilting in the robot. This observation suggests that low-cost bipedal robots driven by servo motors exhibit low stiffness characteristics in their joints.

A reinforcement learning enhanced pseudo-inverse approach to self-collision avoidance of redundant robots.

Introduction: Redundant robots offer greater flexibility compared to non-redundant ones but are susceptible to increased collision risks when the end-effector approaches the robot's own links. Redundant degrees of freedom (DoFs) present an opportunity for collision avoidance; however, selecting an appropriate inverse kinematics (IK) solution remains challenging due to the infinite possible solutions. Methods: This study proposes a reinforcement learning (RL) enhanced pseudo-inverse approach to address self-collision avoidance in redundant robots. The RL agent is integrated into the redundancy resolution process of a pseudo-inverse method to determine a suitable IK solution for avoiding self-collisions during task execution. Additionally, an improved replay buffer is implemented to enhance the performance of the RL algorithm. Results: Simulations and experiments validate the effectiveness of the proposed method in reducing the risk of self-collision in redundant robots. Conclusion: The RL enhanced pseudo-inverse approach presented in this study demonstrates promising results in mitigating self-collision risks in redundant robots, highlighting its potential for enhancing safety and performance in robotic systems.

Inverse kinematics solution and control method of 6-degree-of-freedom manipulator based on deep reinforcement learning.

The advent of Industry 4.0 has significantly promoted the field of intelligent manufacturing, which is facilitated by the development of new technologies are emerging. Robot technology and robot intelligence methods have rapidly developed and been widely applied. Manipulators are widely used in industry, and their control is a crucial research topic. The inverse kinematics solution of manipulators is an important part of manipulator control, which calculates the joint angles required for the end effector to reach a desired position and posture. Traditional inverse kinematics solution algorithms often face the problem of insufficient generalization, and iterative methods have challenges such as large computation and long solution time. This paper proposes a reinforcement learning-based inverse kinematics solution algorithm, called the MAPPO-IK algorithm. The algorithm trains the manipulator agent using the MAPPO algorithm and calculates the difference between the end effector state of the manipulator and the target posture in real-time by designing a reward mechanism, while considering Gaussian distance and cosine distance. Through experimental comparative analysis, the feasibility, computational efficiency, and superiority of this reinforcement learning algorithm are verified. Compared with traditional inverse kinematics solution algorithms, this method has good generalization and supports real-time computation, and the obtained result is a unique solution. Reinforcement learning algorithms have better adaptability to complex environments and can handle different sudden situations in different environments. This algorithm also has the advantages of path planning, intelligent obstacle avoidance, and other advantages in dynamically processing complex environmental scenes.

Muscle-Tendon Unit Length Measurement Using 3D Ultrasound in Passive Conditions: OpenSim Validation and Development of Personalized Models.

This study investigated the validity of using OpenSim to measure muscle-tendon unit (MTU) length of the bi-articular lower limb muscles in several postures (shortened, lengthened, a combination of shortened and lengthened involving both joints, neutral and standing) using 3D freehand ultrasound (US), and to propose new personalized models. MTU length was measured on 14 participants and 6 bi-articular muscles (semimembranosus SM, semitendinosus ST, biceps femoris BF, rectus femoris RF, gastrocnemius medialis GM and gastrocnemius lateralis GL), considering 5 to 6 postures. MTU length was computed using OpenSim with three different models: OS (the generic OpenSim scaled model), OS + INSER (OS with personalized 3D US MTU insertions), OS + INSER + PATH (OS with personalized 3D US MTU insertions and path obtained from one posture). Significant differences in MTU length were found between OS and 3D US models for RF, GM and GL (from - 6.3 to 10.9%). Non-significant effects were reported for the hamstrings, notably for the ST (- 1.5%) and BF (- 1.9%), while the SM just crossed the alpha level (- 3.4%, p = 0.049). The OS + INSER model reduced the magnitude of bias by an average of 4% for RF, GM and GL. The OS + INSER + PATH model showed the smallest biases in length estimates, which made them negligible and non-significant for all the MTU (i.e. ≤ 2.2%). A 3D US pipeline was developed and validated to estimate the MTU length from a limited number of measurements. This opens up new perspectives for personalizing musculoskeletal models using low-cost user-friendly devices.

A leg model based on anatomical landmarks to study 3D joint kinematics of walking in Drosophila melanogaster.

Walking is the most common form of how animals move on land. The model organism Drosophila melanogaster has become increasingly popular for studying how the nervous system controls behavior in general and walking in particular. Despite recent advances in tracking and modeling leg movements of walking Drosophila in 3D, there are still gaps in knowledge about the biomechanics of leg joints due to the tiny size of fruit flies. For instance, the natural alignment of joint rotational axes was largely neglected in previous kinematic analyses. In this study, we therefore present a detailed kinematic leg model in which not only the segment lengths but also the main rotational axes of the joints were derived from anatomical landmarks, namely, the joint condyles. Our model with natural oblique joint axes is able to adapt to the 3D leg postures of straight and forward walking fruit flies with high accuracy. When we compared our model to an orthogonalized version, we observed that our model showed a smaller error as well as differences in the used range of motion (ROM), highlighting the advantages of modeling natural rotational axes alignment for the study of joint kinematics. We further found that the kinematic profiles of front, middle, and hind legs differed in the number of required degrees of freedom as well as their contributions to stepping, time courses of joint angles, and ROM. Our findings provide deeper insights into the joint kinematics of walking in Drosophila, and, additionally, will help to develop dynamical, musculoskeletal, and neuromechanical simulations.

NUMERICALLY STABLE SOLUTION TO THE 6R PROBLEM OF INVERSE KINEMATICS.

In this paper, a stable and accurate algorithm to compute all solutions of the inverse kinematics problem of a 6 revolute manipulator chain is presented. A system of equations is constructed based on the fundamental closure conditions, leading to a closed algebraic system of 20 equations involving 16 quantities, composed of trigonometric functions of five among the six unknown joint angles. Two among these five are stably eliminated using singular value decomposition (SVD) avoiding the need to consider special cases. The resulting system of equations involving three unknowns is solved by conversion to a generalized eigenvalue problem. The remaining three unknown angles are obtained using the previously computed pseudoinverse. In this formulation we exploit the inherently complex form of the system reducing it to 10 complex equations in 9 quantities, which substantially accelerates the SVD computation. The method's robustness is demonstrated through a comparison to current methods and several examples including known problematic cases where some axis or link lengths vanish, or some joint angles are 180 degrees, as well as cases where multiple eigenvalues arise.

Puncture and Needle Picking Action of Venipuncture Manipulator / 医用生物力学

Objective To propose a new multi-joint series venipuncture system, explore the mechanics and kinematics-based related control problems involved in needle insertion and needle picking during the puncture process, and verify feasibility of this system. Methods A puncture manipulator was built, and needle displacement control algorithm was proposed by combing with the puncture mechanics model. The the forward kinematics was calculated by using DH method, so as to obtain the tip coordinates. Then the inverse kinematics was calculated by using the geometric method. The forward and inverse processes were closely connected. The position error of the end coordinates before and after needle picking was compared by using the method of kinematics positive solution-inverse solution-re-positive solution. Finally, experimental verification and simulation were conducted by combining with the physical object. Results Through simulation and experiments, accuracy of the theoretical model was verified. The needle insertion algorithm could be used to achieve success with only one needle insertion, which provided theoretical basis for the control of robot arm. The position error before and after needle picking could be controlled within 1 mm from the end trajectory. The end needle tip of robot arm was almost kept fixed during the needle picking process. Therefore, this needle picking scheme was feasible and could basically verify that the needle picking action of robot arm met the accuracy and safety requirements. Conclusions The venipuncture manipulator truly simulates the needle insertion and needle picking action during the puncture process, and can safely and accurately realize the needle insertion and needle picking action with needle tip as the fixed point, indicating that it has certain clinical value.

Structure Design of Hip Joint Parallel Rehabilitation Exoskeleton / 中国医疗器械杂志

At present, most of the research on hip exoskeleton robots adopts the method of decoupling analysis of hip joint motion, decoupling the ball pair motion of hip joint into rotational motion on sagittal plane, coronal plane and cross section, and designing it into series mechanism. Aiming at the problems of error accumulation and man-machine coupling in series mechanism, a parallel hip rehabilitation exoskeleton structure is proposed based on the bionic analysis of human hip joint. The structure model is established and the kinematics analysis is carried out. Through the OpenSim software, the curve of hip flexion and extension, adduction and abduction angle in a gait cycle is obtained. The inverse solution of the structure is obtained by the D-H coordinate system method. The gait data points are selected and compared with the inverse solution obtained by ADAMS software simulation. The results show that the inverse solution expression is correct. The parallel hip exoskeleton structure can meet the requirements of the rotation angle of the hip joint of the human body, and can basically achieve the movement of the hip joint, which is helpful to improve the human-computer interaction performance of the exoskeleton.

Follow control of upper limb rehabilitation training based on Kinect and NAO robot / 生物医学工程学杂志

Gesture imitation is a common rehabilitation strategy in limb rehabilitation training. In traditional rehabilitation training, patients need to complete training actions under the guidance of rehabilitation physicians. However, due to the limited resources of the hospital, it cannot meet the training and guidance needs of all patients. In this paper, we proposed a following control method based on Kinect and NAO robot for the gesture imitation task in rehabilitation training. The method realized the joint angles mapping from Kinect coordination to NAO robot coordination through inverse kinematics algorithm. Aiming at the deflection angle estimation problem of the elbow joint, a virtual space plane was constructed and realized the accurate estimation of deflection angle. Finally, a comparative experiment for deflection angle of the elbow joint angle was conducted. The experimental results showed that the root mean square error of the angle estimation value of this method in right elbow transverse deflection and vertical deflection directions was 2.734° and 2.159°, respectively. It demonstrates that the method can follow the human movement in real time and stably using the NAO robot to show the rehabilitation training program for patients.

Solution for Movement Mode of Lower Limb Rehabilitation Robot Adapted to Individual Differences / 中国康复理论与实践

Objective:To solve the movement mode adapting to individual differences for the trajectory planning of lower limb rehabilitation robots. Methods:After summarizing the six movement modes of the lower limb rehabilitation robot, according to the multi-rigid body theory of the human body, the exoskeleton of the lower limb rehabilitation robot was simplified into a two-bar linkage mechanism, the inverse kinematics analysis of the motion mode was performed, and the motion pattern solving system was designed based on C#. Results:The motion mode joint angle value calculated based on the C# motion mode solving system was transmitted to the upper computer, and the six motion modes were successfully applied to the lower limb rehabilitation robot. Through the inversion kinematics analysis of the motor model, the C#-designed motion mode solving system could solve the motorized joint angle values that adapted to individual of different leg lengths with lower extremity motor dysfunction. Through physical prototype experiments, the lower limb rehabilitation robot could drive the human body model for rehabilitation training according to the planned exercise mode. The actual joint angle curve and the theoretical joint angle curve were basically coincident, the joint angle error was small. Conclusion:The individual difference motion pattern solution is valid and feasible.

Establishment and Verification of Personalized Musculoskeletal Model Based on nmsBuilder and OpenSim / 医用生物力学

Objective To establish a personalized musculoskeletal multi-body dynamics model of total knee replacement (TKR) by two software nmsBuilder and OpenSim, and verify this established model by using bouncy and medthrust gait patterns. Methods Based on skeletal data from a patient, the body, skeletal landmark clouds and muscular landmark clouds were established for automatically generating reference systems and muscles. The musculoskeletal model generated by nmsBuilder was introduced into OpenSim, and inverse kinematics, static optimization and knee joint force analysis were performed successively. Finally, the model was driven by bouncy gait and medthrust gait respectively, and the results were compared with experimental measurements. Results Except for the lateral joint contact forces, the predicted magnitude and trend of knee joint contact forces by the model had a good agreement with the experimental data, and the constructed skeletal muscle multi-body dynamics model could be used for knee joint research. Conclusions The established musculoskeletal multi-body dynamics model could predict the medial, lateral and total tibiofemoral joint contact forces simultaneously by inputting the marker positions and the ground reaction forces. The research ideas of this study can provide references for designing personalized knee prostheses for TKR patient.

Simulation and experimental study on trajectory tracking of the assistive standing-up robot / 医用生物力学

Objective To establish a new trajectory tracking algorithm combined with trapezoidal velocity, so as to realize the trajectory control of the assistive standing-up robot and help subjects complete the standing-up training. Methods Forces of the assistive standing-up robot acting on subjects were analyzed by deducing the force and moment balance equations. According to the interpolation points of the target curve, trapezoidal velocity and current position points of the end-effector, the trajectory tracking algorithm of the assistive standing-up robot was developed, and a simulation platform was built up by Simulink/Stateflow software. Based on the established Xpc target and host computer, assistive standing-up robot and 3D motion analysis system, trajectory tracking of the straight line, curves in different shapes, standing-up curve of the subjects were tested. Parameters that affected the velocity and accuracy of trajectory tracking as well as the differences in trapezoidal velocity and standing-up velocity were discovered. Results Accurate positon control of the assistive standing-up robot was achieved by trajectory tracking algorithm. The standing-up trajectory curve and trapezoidal velocity could meet the requirement of standing-up velocity for the subjects and fulfill their requirements for different curve shapes and velocities. Conclusions The assistive standing-up robot using trajectory tracking algorithm combined with trapezoidal velocity can accurately track the target curves without limitation of curve shapes, and help the standing-up training for subjects. The established simulation and test platform in consideration of different subjects’ standing-up trajectory curve, velocity and accelaraion will assist standing-up more effectively.