Joint Velocity Research Articles

Motion Planning of Nonholonomic Mobile Manipulators with Manipulability Maximization Considering Joints Physical Constraints and Self-Collision Avoidance

The motion of nonholonomic mobile manipulators (NMMs) is inherently constrained by joint limits, joint velocity limits, self-collisions and singularities. Most motion planning algorithms consider some of the aforementioned constraints, however, a unified framework to deal with all of them is lacking. This paper proposes a motion planning solution for the kinematic trajectory tracking of redundant NMMs that include all the constraints needed for practical implementation, which improves the manipulability of both the entire system and the manipulator to prevent singularities. Experiments using a 10-DOF NMM demonstrate the good performance of the proposed method for executing 6-DOF trajectories while satisfying all the constraints and simultaneously maximizing manipulability.

Does fatigue affect the kinematics of shooting in female basketball?

ABSTRACT Few studies investigated the influence of fatigue on the change of kinematic parameters in basketball shooting regarding female players. This study aimed to determine the difference of kinematics in basketball jump shot between non-fatigue and fatigue conditions. Thirty-two professional female basketball players volunteered to take part in the study (age: 22.11 ± 4.92 years; height: 173.99 ± 7.06 cm; weight: 67.89 ± 5.65 kg). 3D motion analysis using an inertial suit and a smart ball were performed for measuring the kinematic parameters. The results demonstrated that there were no significant differences in angular velocity of ankle, knee and hip joints. Conversely, differences in angular velocity of elbow (p = 0.036) and wrist (p = 0.002) were detected. In addition, the results showed that the release height and entry angle of the ball significantly decreased in fatigue condition, suggesting that coaches need to include in the training process exercise that is similar in terms of fatigue and performance to the situational condition during the game as these two variables play an important role in the determination of shooting efficiency.

Imitation Learning With Additional Constraints on Motion Style Using Parametric Bias

Imitation learning is one of the methods for reproducing human demonstration adaptively in robots. So far, it has been found that generalization ability of the imitation learning enables the robots to perform tasks adaptably in untrained environments. However, motion styles such as motion trajectory and the amount of force applied depend largely on the dataset of human demonstration, and settle down to an average motion style. In this study, we propose a method that adds parametric bias to the conventional imitation learning network and can add constraints to the motion style. By experiments using PR2 and the musculoskeletal humanoid MusashiLarm, we show that it is possible to perform tasks by changing its motion style as intended with constraints on joint velocity, muscle length velocity, and muscle tension.

Human Posture Prediction During Physical Human-Robot Interaction

When a human is interacting physically with a robot to accomplish a task, his/her posture is inevitably influenced by the robot movement. Since the human is not controllable, an active robot imposing a collaborative trajectory should predict the most likely human posture. This prediction should consider individual differences and preferences of movement execution, and it is necessary to evaluate the impact of the robot's action from an ergonomics standpoint. Here, we propose a method to predict, in probabilistic terms, the human postures of an individual for a given robot trajectory executed in a collaborative scenario. We formalize the problem as the prediction of the human joints velocity given the current posture and robot end-effector velocity. The key idea of our approach is to learn the distribution of the null space of the Jacobian and the weights of the weighted pseudo-inverse from demonstrated human movements: both carry information about human postural preferences, to leverage redundancy and ensure that the predicted posture will be coherent with the end-effector position. We validate our approach in a simulated toy problem and on two real human-robot interaction experiments where a human is physically interacting with a Franka robot.

Digital twin product lifecycle system dedicated to the constant velocity joint

The constant velocity joint for the automobile is a key factor in realizing vehicle steering and propulsion, whose quality has a direct impact on the safety, maneuverability and comfort. To improve the manufacturing quality and later-stage maintenance efficiency of the constant velocity joint, a multi-dimensional digital twin model dedicated to product lifecycle of the constant velocity joint is established in this paper. Combing with the actual manufacturing process and process of the constant velocity joint, the three-dimensional(3D) modeling of its parts is established. Using computer simulation technology to reduce its production and test costs, so that the digital twin model has the ability to diagnose faults and optimize design. Using algorithm analysis and experience evaluation to process real-time data to achieve the purpose of predictive maintenance of constant velocity joint. The customized software based on CATIA is developed, and the digital twin model dedicated to product lifecycle of the constant velocity joint is verified which realize the balance between product standardization and manufacturing flexibility.

A Robot-Centered Path-Planning Algorithm for Multidirectional Additive Manufacturing for WAAM Processes and Pure Object Manipulation

Previous algorithms for slicing, path planning or trajectory planning of additive manufacturing cannot be used consistently for multidirectional additive manufacturing with pure object manipulation in wire-arc additive manufacturing. This work presents a novel path planning approach that directly takes robot kinematics into account and thus ensures the reachability of all critical path poses. In an additional step, the planned path segments are smoothed so that joint velocity limits are respected. It is shown that the implemented path planner generates executable robot paths and at the same time maintains the process quality (in this case, sufficient coverage of the slice area). While the introduced method enables the generation of reachable printing paths, the smoothing algorithm allows for the execution of the path with respect to the robot’s velocity limits and at the same time improves the slice coverage. Future experiments will show the realization of the real robot setup presented.

Method for Robot Manipulator Joint Wear Reduction by Finding the Optimal Robot Placement in a Robotic Cell

We describe a method for robotic cell optimization by changing the placement of the robot manipulator within the cell in applications with a fixed end-point trajectory. The goal is to reduce the overall robot joint wear and to prevent uneven joint wear when one or several joints are stressed more than the other joints. Joint wear is approximated by calculating the integral of the mechanical work of each joint during the whole trajectory, which depends on the joint angular velocity and torque. The method relies on using a dynamic simulation for the evaluation of the torques and velocities in robot joints for individual robot positions. Verification of the method was performed using CoppeliaSim and a laboratory robotic cell with the collaborative robot UR3. The results confirmed that, with proper robot base placement, the overall wear of the joints of a robotic arm could be reduced from 22% to 53% depending on the trajectory.

The Math of Tasks: A Domain Specific Language for Constraint-Based Task Specification

This paper proposes a Domain Specific Language to describe in a synthetic and comprehensive way, complex control problems for robotic systems. The proposed language, named Math of Tasks, abstracts from the mathematical description of the problem, which heavily depends on the particular algorithm chosen to solve it, relying on a set of operators and entities which instead, composed together, describes the behavior of the control problem. The Math of Tasks can describe any type of instantaneous controller regardless of the particular controlled variable (e.g., joint velocity, joint acceleration, contact forces). This paper shows the convenience of the proposed formalism using examples from classical control problems for different types of robotic platforms such as manipulators, humanoid bipeds, and quadrupeds.

Eulerian formulation for the triboelectric charging of polydisperse powder flows

We propose an Eulerian formulation for the triboelectric charging of powder flows. Recently, continuum descriptions of the charging of monodisperse and bidisperse powder appeared. Now, we propose the first Eulerian formulation for this type of flows that can fully account for polydisperse particle size distributions. To this end, the joint particle size, velocity, and charge distribution function are solved through the direct quadrature method of moments. The newly established approach includes the uptake of electrostatic charge by the particles when contacting a solid wall and the exchange of charge in-between particles during collisions. The electrostatic field induced by the charge of the particles and the drag forces of the surrounding gas affects the particle dynamics. Test cases of two-dimensional steady channel flows demonstrate the charging of the flow from the walls toward the center and in the downstream direction. Further, the method predicts the effect of variations of the particle size and initial velocity distribution and the charge diffusion coefficient. The ability to handle polydispersity is a step toward the simulation of the electric charge buildup of real powder flows in full-scale technical applications.

Adaptive most joint selection and covariance descriptions for a robust skeleton-based human action recognition

In this paper, we propose two effective manners of utilizing skeleton data for human action recognition (HAR). The proposed method on one hand takes advantage of the skeleton data thanks to their robustness to human appearance change as well as the real-time performance. On the other hand, it avoids inherent drawbacks of the skeleton data such as noises, incorrect human skeleton estimation due to self-occlusion of human pose. To this end, in terms of feature designing, we propose to extract covariance descriptors from joint velocity and combine them with those of joint position. In terms of 3-D skeleton-based activity representation, we propose two schemes to select the most informative joints. The proposed method is evaluated on two benchmark datasets. On the MSRAction-3D dataset, the proposed method outperformed different hand-designed features-based methods. On the challenging dataset CMDFall, the proposed method significantly improves accuracy when compared with techniques based on recent neuronal networks. Finally, we investigate the robustness of the proposed method via a cross-dataset evaluation.

Dynamic visual servoing with Kalman filter-based depth and velocity estimator

Camera calibration error, vision latency, nonlinear dynamics, and so on present a major challenge for designing the control scheme for a visual servoing system. Although many approaches on visual servoing have been proposed, surprisingly, only a few of them have taken into account system dynamics in the control design of a visual servoing system. In addition, the depth information of feature points is essential in the image-based visual servoing architecture. As a result, to cope with the aforementioned problems, this article proposes a Kalman filter-based depth and velocity estimator and a modified image-based dynamic visual servoing architecture that takes into consideration the system dynamics in its control design. In particular, the Kalman filter is exploited to deal with the problems caused by vision latency and image noise so as to facilitate the estimation of the joint velocity of the robot using image information only. Moreover, in the modified image-based dynamic visual servoing architecture, the computed torque control scheme is used to compensate for system dynamics and the Kalman filter is used to provide accurate depth information of the feature points. Results of visual servoing experiments conducted on a two-degree of freedom planar robot verify the effectiveness of the proposed approach.

Dissociating Sensorimotor Recovery and Compensation During Exoskeleton Training Following Stroke.

The quality of arm movements typically improves in the sub-acute phase of stroke affecting the upper extremity. Here, we used whole arm kinematic analysis during reaching movements to distinguish whether these improvements are due to true recovery or to compensation. Fifty-three participants with post-acute stroke performed ∼80 reaching movement tests during 4 weeks of training with the ArmeoSpring exoskeleton. All participants showed improvements in end-effector performance, as measured by movement smoothness. Four ArmeoSpring angles, shoulder horizontal (SH) rotation, shoulder elevation (SE), elbow rotation, and forearm rotation, were recorded and analyzed. We first characterized healthy joint coordination patterns by performing a sparse principal component analysis on these four joint velocities recorded during reaching tests performed by young control participants. We found that two dominant joint correlations [SH with elbow rotation and SE with forearm rotation] explained over 95% of variance of joint velocity data. We identified two clusters of stroke participants by comparing the evolution of these two correlations in all tests. In the “Recoverer” cluster (N = 19), both joint correlations converged toward the respective correlations for control participants. Thus, Recoverers relearned how to generate smooth end-effector movements while developing joint movement patterns similar to those of control participants. In the “Compensator” cluster (N = 34), at least one of the two joint correlations diverged from the corresponding correlation of control participants. Compensators relearned how to generate smooth end-effector movements by discovering various new compensatory movement patterns dissimilar to those of control participants. New compensatory patterns included atypical decoupling of the SE and forearm joints, and atypical coupling of the SH rotation and elbow joints. There was no difference in clinical impairment level between the two groups either at the onset or at the end of training as assessed with the Upper Extremity Fugl-Meyer scale. However, at the start of training, the Recoverers showed significantly faster improvements in end-effector movement smoothness than the Compensators. Our analysis can be used to inform neurorehabilitation clinicians on how to provide movement feedback during practice and suggest avenues for refining exoskeleton robot therapy to reduce compensatory patterns.

Motion estimation of elbow joint from sEMG using continuous wavelet transform and back propagation neural networks

Estimation of continuous joint movement of human arm is significant in human-robot interaction. In this study, we proposed an estimating method combined continuous wavelet transform (CWT) and back propagation neural networks (BPNN) to estimate the joint angles and velocities of human arm from surface electromyography (sEMG) signals during the human-robot interaction. The proposed method was evaluated by comparing to other existing methods and the results derived by using a goniometer through experiments. Nine conditions with different motion patterns and force levels are conducted in the experiments. The root mean squared error (RMSE) and the correlation coefficient (CC) were used as the evaluation indices. Experimental results showed that the estimated angles and velocities of elbow joint were well match to the “true” values derived by the goniometer. The average RMSE of estimated angle and velocity derived by the proposed method was 8.78 degree and 9.59 degree/second, respectively. The average RMSE in both the joint angle and velocity for the proposed method was the smallest comparing to the back propagation neural networks, the higher-order polynomial, and the support vector regression.

A novel 3D path following control framework for robots performing surface finishing tasks

We describe a novel 3D path following control framework for articulated robots in applications where constant speed travel along a path is desirable, such as robotic surface finishing tasks. Given the desired robot configuration sequence with a list of waypoints along a path, a trajectory optimization scheme based on direct collocation is proposed to determine the Cartesian path and the maximum constant translation speed that are dynamically feasible. Employing the Hermite–Simpson collocation method, a Cartesian path is developed that not only preserves the characteristics of the original motion sequence but also satisfies the physical requirements of the robot kinematics and dynamics. Since joint velocity control is quite common in many industrial robots, we consider a 3D kinematic control in the robot tool frame with control inputs as rate of change of orientation. The objective for the translation motion is to achieve constant speed along the path tangent direction, and that of the orientation control is to orient the robot properly based on the path provided by a converging path planner. We describe the optimization procedure employed with the direct collocation method to obtain the desired Cartesian path, an arc-length based re-parametrization of the desired path, and a path planner that provides a converging path to the desired path. To perform the surface finishing operation, we further present the joint space control law that is converted from the synthesis of the proposed path following and impedance force control in the tool frame. To verify and evaluate the performance of the proposed framework, we have conducted extensive experiments with a six degrees-of-freedom (DOF) industrial robot for several paths that can be employed for surface finishing of a variety of industrial parts.

Are Torque-Driven Simulation Models of Human Movement Limited by an Assumption of Monoarticularity?

Subject-specific torque-driven computer simulation models employing single-joint torque generators have successfully simulated various sports movements with a key assumption that the maximal torque exerted at a joint is a function of the kinematics of that joint alone. This study investigates the effect on model accuracy of single-joint or two-joint torque generator representations within whole-body simulations of squat jumping and countermovement jumping. Two eight-segment forward dynamics subject-specific rigid body models with torque generators at five joints are constructed—the first model includes lower limb torques, calculated solely from single-joint torque generators, and the second model includes two-joint torque generators. Both models are used to produce matched simulations to a squat jump and a countermovement jump by varying activation timings to the torque generators in each model. The two-joint torque generator model of squat and countermovement jumps matched measured jump performances more closely (6% and 10% different, respectively) than the single-joint simulation model (10% and 24% different, respectively). Our results show that the two-joint model performed better for squat jumping and the upward phase of the countermovement jump by more closely matching faster joint velocities and achieving comparable amounts of lower limb joint extension. The submaximal descent phase of the countermovement jump was matched with similar accuracy by the two models (9% difference). In conclusion, a two-joint torque generator representation is likely to be more appropriate for simulating dynamic tasks requiring large joint torques and near-maximal joint velocities.

Hydraulic Proportional Actuated Arm Pose Control Based on Joint Velocity

Hydraulic Proportional Actuated Arm Pose Control Based on Joint Velocity

Observer-based fixed-time tracking control for space robots in task space

The problem of task-space tracking control of a free-floating space robot is addressed in this paper. Based on a fixed-time extended state observer (FXESO), a fixed-time position and attitude control (FXPAC) method is developed. The attitude of the end-effector is represented in modified Rodrigues parameters (MRPs). The FXESO provides the estimation of the joint velocity and the lumped disturbance for the control system. Based on the backstepping technique and a power integrator, the FXPAC method is designed. This control scheme can guarantee tracking errors converging to a neighborhood of the origin within a fixed time in the presence of the external disturbance. Moreover, since the inverse of the generalized Jacobian matrix is involved in the controller, it is necessary to consider dynamic singularities. To handle these singularities, a method of combining the singularity separation and damped reciprocal (SSDR) is applied to the space robot. Numerical simulations are presented to demonstrate the effectiveness of the proposed method.

Joint range and velocity estimation for integration of radar and communication based on multi‐symbol OFDM radar pulses

Joint range and velocity estimation for integration of radar and communication based on multi‐symbol OFDM radar pulses

NEO: A Novel Expeditious Optimisation Algorithm for Reactive Motion Control of Manipulators

We present NEO, a fast and purely reactive motion controller for manipulators which can avoid static and dynamic obstacles while moving to the desired end-effector pose. Additionally, our controller maximises the manipulability of the robot during the trajectory, while avoiding joint position and velocity limits. NEO is wrapped into a strictly convex quadratic programme which, when considering obstacles, joint limits, and manipulability on a 7 degree-of-freedom robot, is generally solved in a few ms. While NEO is not intended to replace state-of-the-art motion planners, our experiments show that it is a viable alternative for scenes with moderate complexity while also being capable of reactive control. For more complex scenes, NEO is better suited as a reactive local controller, in conjunction with a global motion planner. We compare NEO to motion planners on a standard benchmark in simulation and additionally illustrate and verify its operation on a physical robot in a dynamic environment. We provide an open-source library which implements our controller.

Muscle Force Patterns in Lower Extremity Muscles for Elite Discus Throwers, Javelin Throwers and Shot-Putters - A Case Study.

Optimal release variables, as well as the kinematics and kinetics of athletes, are crucial for the maximization of throwing distance in athletics. Mathematical models and simulations allow throwing techniques to be studied. However, muscle force patterns and the contribution of specific muscle groups in athletics throwing events are not well understood and require detailed research. In this study, important variables of the muscle force generated during the javelin, discus and shot put events were determined using OpenSim software. Musculoskeletal simulations were carried out based on kinematic and kinetic data collected using the Vicon system and Kistler plates with the help of nine top Polish athletes (three in each event). OpenSim software was used to calculate muscle forces and joint velocities. For each discipline, it was found that the main muscle groups involved in the throwing movement were better at distinguishing throwers than joint velocities. The contribution of right ankle plantar flexors at the beginning of the final acceleration phase as well as left hip extensors at the end of the final acceleration phase was given special attention. This work provides a better understanding of the techniques used in athletics throws. Musculoskeletal simulations of throwing styles might help coaches analyze the techniques of individual athletes, resulting in better adjustment of training programmes and injury prevention protocols.