Human motion behavior while interacting with an industrial robot

For robots to execute their navigation tasks both fast and safely in the presence of humans, it is necessary to make predictions about the route those humans intend to follow. Within this work, a model-based method is proposed that relates human motion behavior perceived from RGBD input to the constraints imposed by the environment by considering typical human routing alternatives. Multiple hypotheses about routing options of a human towards local semantic goal locations are created and validated, including explicit collision avoidance routes. It is demonstrated, with real-time, real-life experiments, that a coarse discretization based on the semantics of the environment suffices to make a proper distinction between a person going, for example, to the left or the right on an intersection. As such, a scalable and explainable solution is presented, which is suitable for incorporation within navigation algorithms.

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From Human Motion Capture to Industrial Robot Imitation

Robotics and Mechatronics - Mechanisms and Machine Science ◽

10.1007/978-3-319-22368-1_30 ◽

2015 ◽

pp. 301-312

Author(s):

P. Laguillaumie ◽

M. A. Laribi ◽

P. Seguin ◽

P. Vulliez ◽

A. Decatoire ◽

...

Keyword(s):

Motion Capture ◽

Industrial Robot ◽

Human Motion ◽

Human Motion Capture

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Design and Integration of a Torque Sensor Beam Based Masterarm

10.1115/imece2001/dsc-24618 ◽

2001 ◽

Author(s):

Sooyong Lee ◽

Yoon Sang Kim ◽

Changhyun Cho ◽

Munsang Kim

Keyword(s):

Industrial Robot ◽

Weight Ratio ◽

Human Motion ◽

Serial Links ◽

Joint Movement ◽

Torque Sensor ◽

Upper Arm ◽

Maximum Range ◽

Force Reflection ◽

Electric Brake

Abstract A new concept of the exoskeleton-type masterarm, composed of serial links, is introduced in this paper. To provide maximum range of human motion, several redundant joints are added to the serial links. In order to reduce the number of joints to be measured, kinematics of serial links was taken into consideration in design. Three measurable, controllable joints and three redundant free joints are used for the upper arm (shoulder), similarly to the forearm (wrist) while one measurable, controllable joint is used for the elbow. In particular, a torque sensor beam is designed for fine force reflection using the strain gauge. It detects the torque as well as its direction applied by the human operator, which allows the electric brake to be used as an actuator for force reflection. The electric brake constrains the joint movement so that the operator can feel the force. This electric brake outperforms the motor in terms of torque/weight ratio and makes the device light and compact. This masterarm measures the movement of the operator’s arm precisely, and it can be used for teleoperation with a slave robot, or as a motion planner for an industrial robot.

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Real-Time Human Motion Behavior Detection via CNN Using mmWave Radar

IEEE Sensors Letters ◽

10.1109/lsens.2018.2889060 ◽

2019 ◽

Vol 3 (2) ◽

pp. 1-4 ◽

Cited By ~ 11

Author(s):

Renyuan Zhang ◽

Siyang Cao

Keyword(s):

Real Time ◽

Human Motion ◽

Behavior Detection ◽

Motion Behavior

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A real-time trajectory modification algorithm

Robotica ◽

10.1017/s0263574701003393 ◽

2001 ◽

Vol 19 (4) ◽

pp. 395-405 ◽

Cited By ~ 2

Author(s):

Vadim Rogozin ◽

Yael Edan ◽

Tamar Flash

Keyword(s):

Real Time ◽

Target Location ◽

Time Algorithm ◽

Initial Position ◽

Human Motion ◽

Movement Direction ◽

Switch Time ◽

Velocity Constraints ◽

The Mathematical Model ◽

Motion Behavior

This paper presents a real-time algorithm for modifying the trajectory of a manipulator approaching a moving target. The algorithm is based on the superposition scheme; a model developed based on human motion behavior. The algorithm generates a smooth trajectory toward the new target by calculating the vectorial sum between the first trajectory (initial position and first target) and second trajectory (between first and second target location). The algorithm searches for the switch hme that will result in a minimum time trajectory. The idea of the algorithm is to define some domain where the optimal switching time can be found, reduce this domain as much as possible to decrease the number of the points that must be checked and try every remaining candidate in this domain to find numerically the best (optimal) switch time. The algorithm was implemented on an Adept-one robotic system taking into account velocity constraints. The actual velocity profile was found to be less smooth than specified by the mathematical model. When the switch occurs at the middle of the trajectory when the speed is close to its maximum, the change in the movement direction is performed more gently.

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Metrological Evaluation of Human–Robot Collaborative Environments Based on Optical Motion Capture Systems

Sensors ◽

10.3390/s21113748 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3748

Author(s):

Leticia González ◽

Juan C. Álvarez ◽

Antonio M. López ◽

Diego Álvarez

Keyword(s):

Motion Capture ◽

Motion Tracking ◽

Industrial Robot ◽

Human Motion ◽

Factors Affecting ◽

Collaborative Environment ◽

Accuracy And Precision ◽

Optical Motion ◽

Optical Motion Capture ◽

Metrological Evaluation

In the context of human–robot collaborative shared environments, there has been an increase in the use of optical motion capture (OMC) systems for human motion tracking. The accuracy and precision of OMC technology need to be assessed in order to ensure safe human–robot interactions, but the accuracy specifications provided by manufacturers are easily influenced by various factors affecting the measurements. This article describes a new methodology for the metrological evaluation of a human–robot collaborative environment based on optical motion capture (OMC) systems. Inspired by the ASTM E3064 test guide, and taking advantage of an existing industrial robot in the production cell, the system is evaluated for mean error, error spread, and repeatability. A detailed statistical study of the error distribution across the capture area is carried out, supported by a Mann–Whitney U-test for median comparisons. Based on the results, optimal capture areas for the use of the capture system are suggested. The results of the proposed method show that the metrological characteristics obtained are compatible and comparable in quality to other methods that do not require the intervention of an industrial robot.

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Replicating dynamic humerus motion using an industrial robot

PLoS ONE ◽

10.1371/journal.pone.0242005 ◽

2020 ◽

Vol 15 (11) ◽

pp. e0242005

Author(s):

Klevis Aliaj ◽

Gentry M. Feeney ◽

Balakumar Sundaralingam ◽

Tucker Hermans ◽

K. Bo Foreman ◽

...

Keyword(s):

Motion Capture ◽

Bone Fracture ◽

Industrial Robot ◽

Human Motion ◽

Optical Tracking ◽

Multiaxial Loading ◽

Computational Pipeline ◽

Rate Dependent ◽

Skin Marker

Transhumeral percutaneous osseointegrated prostheses provide upper-extremity amputees with increased range of motion, more natural movement patterns, and enhanced proprioception. However, direct skeletal attachment of the endoprosthesis elevates the risk of bone fracture, which could necessitate revision surgery or result in loss of the residual limb. Bone fracture loads are direction dependent, strain rate dependent, and load rate dependent. Furthermore, in vivo, bone experiences multiaxial loading. Yet, mechanical characterization of the bone-implant interface is still performed with simple uni- or bi-axial loading scenarios that do not replicate the dynamic multiaxial loading environment inherent in human motion. The objective of this investigation was to reproduce the dynamic multiaxial loading conditions that the humerus experiences in vivo by robotically replicating humeral kinematics of advanced activities of daily living typical of an active amputee population. Specifically, 115 jumping jack, 105 jogging, 15 jug lift, and 15 internal rotation trials—previously recorded via skin-marker motion capture—were replicated on an industrial robot and the resulting humeral trajectories were verified using an optical tracking system. To achieve this goal, a computational pipeline that accepts a motion capture trajectory as input and outputs a motion program for an industrial robot was implemented, validated, and made accessible via public code repositories. The industrial manipulator utilized in this study was able to robotically replicate over 95% of the aforementioned trials to within the characteristic error present in skin-marker derived motion capture datasets. This investigation demonstrates the ability to robotically replicate human motion that recapitulates the inertial forces and moments of high-speed, multiaxial activities for biomechanical and orthopaedic investigations. It also establishes a library of robotically replicated motions that can be utilized in future studies to characterize the interaction of prosthetic devices with the skeletal system, and introduces a computational pipeline for expanding this motion library.

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Trace2trace—A Feasibility Study on Neural Machine Translation Applied to Human Motion Trajectories

Sensors ◽

10.3390/s20123503 ◽

2020 ◽

Vol 20 (12) ◽

pp. 3503

Author(s):

Alessandro Crivellari ◽

Euro Beinat

Keyword(s):

Machine Translation ◽

Computational Linguistics ◽

Information Disclosure ◽

Large Scale ◽

Short Term Memory ◽

Human Mobility ◽

Human Motion ◽

Neural Machine Translation ◽

Crowd Management ◽

Motion Behavior

Neural machine translation is a prominent field in the computational linguistics domain. By leveraging the recent developments of deep learning, it gave birth to powerful algorithms for translating text from one language to another. This study aims to assess the feasibility of transferring the neural machine translation approach into a completely different context, namely human mobility and trajectory analysis. Building a conceptual parallelism between sentences (sequences of words) and motion traces (sequences of locations), we aspire to translate individual trajectories generated by a certain category of users into the corresponding mobility traces potentially generated by a different category of users. The experiment is inserted in the background of tourist mobility analysis, with the goal of translating the motion behavior of tourists belonging to a specific nationality into the motion behavior of tourists belonging to a different nationality. The model adopted is based on the seq2seq approach and consists of an encoder–decoder architecture based on long short-term memory (LSTM) neural networks and neural embeddings. The encoder turns an input location sequence into a corresponding hidden vector; the decoder reverses the process, turning the vector into an output location sequence. The proposed framework, tested on a real-world large-scale dataset, explores an effective attempt of motion transformation between different entities, arising as a potentially powerful source of mobility information disclosure, especially in the context of crowd management and smart city services.

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