Identification of COM Controller of a Human in Stance Based on Motion Measurement and Phase-Space Analysis

This article proposes a process to identify the standing stabilizer, namely, the controller in humans to keep upright posture stable against perturbations. We model the controller as a piecewise-linear feedback system, where the state of the center of mass (COM) is regulated by coordinating the whole body so as to locate the zero-moment point (ZMP) at the desired position. This was developed for humanoid robots and is possibly able to elaborate the fundamental control scheme used by humans to stabilize themselves. Difficulties lie on how to collect motion trajectories in a wide area of the state space for reliable identification and how to identify the piecewise-affine dynamical system. For the former problem, a motion measurement protocol is devised based on the theoretical phase portrait of the system. Regarding the latter problem, some clustering techniques including K-means method and EM (Expectation-and-Maximization) algorithm were examined. We found that a modified K-means method produced the most accurate result in this study. The method was applied to the identification of a lateral standing controller of a human subject. The result of the identification quantitatively supported a hypothesis that the COM-ZMP regulator reasonably models the human’s controller when deviations of the angular momentum about the COM are limited.

Modeling Pedestrian Motion in Crowded Scenes Based on the Shortest Path Principle

In the computer vision field, understanding human dynamics is not only a great challenge but also very meaningful work, which plays an indispensable role in public safety. Despite the complexity of human dynamics, physicists have found that pedestrian motion in a crowd is governed by some internal rules, which can be formulated as a motion model, and an effective model is of great importance for understanding and reconstructing human dynamics in various scenes. In this paper, we revisit the related research in social psychology and propose a two-part motion model based on the shortest path principle. One part of the model seeks the origin and destination of a pedestrian, and the other part generates the movement path of the pedestrian. With the proposed motion model, we simulated the movement behavior of pedestrians and classified them into various patterns. We next reconstructed the crowd motions in a real-world scene. In addition, to evaluate the effectiveness of the model in crowd motion simulations, we created a new indicator to quantitatively measure the correlation between two groups of crowd motion trajectories. The experimental results show that our motion model outperformed the state-of-the-art model in the above applications.

Sampling motion trajectories during hippocampal theta sequences

Efficient planning in complex environments requires that uncertainty associated with current inferences and possible consequences of forthcoming actions is represented. Representation of uncertainty has been established in sensory systems during simple perceptual decision making tasks but it remains unclear if complex cognitive computations such as planning and navigation are also supported by probabilistic neural representations. Here we capitalized on gradually changing uncertainty along planned motion trajectories during hippocampal theta sequences to capture signatures of uncertainty representation in population responses. In contrast with prominent theories, we found no evidence of encoding parameters of probability distributions in the momentary population activity recorded in an open-field navigation task in rats. Instead, uncertainty was encoded sequentially by sampling motion trajectories randomly in subsequent theta cycles from the distribution of potential trajectories. Our analysis is the first to demonstrate that the hippocampus is well equipped to contribute to optimal planning by representing uncertainty.

The Method for Estimating the Maximum Velocity of the AUV Motion with Trajectories Re-Planning According to their Dynamic Properties

The paper considers the problem of adjusting the value of a predetermined velocity required for horizontal motion of autonomous underwater vehicles (AUVs) in an environment containing obstacles, when trajectories change in order to avoid obstacles. Therewith, the velocity estimation generated during the re-planning of the AUVs motion trajectories is the maximum possible and is carried out on the basis of the AUVs dynamics model, considering their dynamic limitations and changes in the parameters of the motion trajectories. The topicality of the task is determined by the need to improve the efficiency of underwater missions in various areas of human activity (environmental monitoring, laying and maintenance of underwater communications, etc.). It depends on the mode of the AUVs motion: their velocity and parameters of the trajectories. The simulation results confirm the efficiency of the proposed method for estimating the maximum possible velocity of the AUVs motion.

Software Platform for the Assisted Research of the Kinematics and Dynamics of Industrial Robots

The paper presents a software platform made with LabVIEWTM for the assisted research of the kinematic and dynamic behavior of industrial robots. The platform comprises a series of virtual instrumentation LabVIEWTM programs (subVI-s) with: the input data modules in the form of several clusters with the parameters of the trapezoidal velocity characteristics of each joint, the axes of movement and the type of each joints, the dimensions of each body, the graph associated to the robot’s structure, the incidence matrices bodies - joints and joints- bodies, as well as the control buttons for movement up or down with or without object in the end- effecter, some modules with 2D characteristics of positions, velocities, accelerations, forces and moments in each joints and also the 3D characteristics of them. The research of the current stage shows that such a complex platform like this was not realized, the current research being limited to the animation of motion trajectories, determining the characteristics of positions, velocities, accelerations, forces and moments without the possibility of changing all motion parameters and robot’s dimensions and without show how these parameters change the behavior. The paper studies the case of an articulated arm type robot, but the platform can be used for any type of robot with four degrees of freedom (DOF).

Assessment of computer vision methods for motion tracking of planar mechanisms

One of the main challenges on the use of planar mechanisms is to verify and monitor that the trajectories described by the mechanism correspond to those originally required. However, very few research studies have focused on tracking and monitoring the motion of target points located on the mechanisms during operation conditions. In this paper, a comparative study to evaluate the performance of several computer vision methods (CVMs) when used in motion tracking of planar mechanisms is presented. The aim is to compare and identify the best CVM, in terms of precision, speed, low cost, and computational performance, to track the movement of planar mechanisms. For this purpose, a case study corresponding to a planar four-bar mechanism is selected and analysed. The results show that the vision methods based on the homogeneous and non-homogeneous solution of the camera calibration matrix are a technological alternative for monitoring motion trajectories of planar mechanisms.

Augmented Reality Assisted Assembly Training Oriented Dynamic Gesture Recognition and Prediction

Augmented reality assisted assembly training (ARAAT) is an effective and affordable technique for labor training in the automobile and electronic industry. In general, most tasks of ARAAT are conducted by real-time hand operations. In this paper, we propose an algorithm of dynamic gesture recognition and prediction that aims to evaluate the standard and achievement of the hand operations for a given task in ARAAT. We consider that the given task can be decomposed into a series of hand operations and furthermore each hand operation into several continuous actions. Then, each action is related with a standard gesture based on the practical assembly task such that the standard and achievement of the actions included in the operations can be identified and predicted by the sequences of gestures instead of the performance throughout the whole task. Based on the practical industrial assembly, we specified five typical tasks, three typical operations, and six standard actions. We used Zernike moments combined histogram of oriented gradient and linear interpolation motion trajectories to represent 2D static and 3D dynamic features of standard gestures, respectively, and chose the directional pulse-coupled neural network as the classifier to recognize the gestures. In addition, we defined an action unit to reduce the dimensions of features and computational cost. During gesture recognition, we optimized the gesture boundaries iteratively by calculating the score probability density distribution to reduce interferences of invalid gestures and improve precision. The proposed algorithm was evaluated on four datasets and proved to increase recognition accuracy and reduce the computational cost from the experimental results.

Vibration Mode and Motion Trajectory Simulations of an Articulated Robot by a Dynamic Model Considering Joint Bearing Stiffness

Articulated robots are widely used in industries because they can perform manufacturing tasks with complicated movements. Higher speed and accuracy of motions are always required to improve the quality and productivity of products. The vibration characteristics of the robots are an important factor to achieve higher speed and accuracy motions. Robots are increasingly being used for machining. The vibration characteristics must also be considered when designing proper cutting conditions for the machining. To design control and cutting strategies for higher speed and accuracy motions or higher productivity of the machining process, it is effective to investigate the vibration characteristics of the robot and develop a mathematical model which can represents the vibration characteristics. The aim of this study is to investigate the vibration characteristics of an architectural robot and develop a mathematical model which can represent the dynamic behavior of the robot. To achieve this, vibration mode of an industrial architectural robot is analyzed based on measured frequency characteristics. According to the results of the modal analysis, it was clarified that the axial and angular stiffness of bearings of each joint of the robot has a significant impact on the vibration characteristics. Therefore, in this study, a mathematical model of the robot is developed considering the joint bearing stiffness. The mathematical model that also considers the kinematics of the robot, stiffness of reduction gears, control system for motors, and disturbance, such as friction and gravity, is introduced into the model. The control system is precisely modeled based on actual control algorithm in accordance with the implemented source codes. Although mass and inertia of the links are obtained from the 3D-CAD model, stiffness and damping parameters of the bearings and reduction gears are identified by matching the measured and simulated frequency responses. It has been confirmed that the model can adequately represents the vibration mode of the actual robot. Circular motion tests were performed to verify the model. Motion trajectories of the end effector were measured and simulated. As a result, it has been confirmed that the developed model is effective to analyze the dynamic behaviors.