scholarly journals Lifting Objects with Power-Assist: Weight-Perception-Based Force Control Concepts to Improve Maneuverability

2011 ◽  
Vol 2-3 ◽  
pp. 277-280 ◽  
Author(s):  
S.M. Mizanoor Rahman ◽  
Ryojun Ikeura ◽  
Soichiro Hayakawa ◽  
Hao Yong Yu
Keyword(s):  
Force Control ◽  
Position Control ◽  
Control Strategies ◽  
Human Subjects ◽  
Weight Perception ◽  
Robot Dynamics ◽  
Robot System ◽  
Actual Weight ◽  
Comparison Results ◽  
Perceived Weight

We developed a 1-DOF power assist robot system to lift objects of different sizes by human subjects. We adopted a hypothesis that weight perception due to inertia might be different from that due to gravity when lifting an object with a power assist robot because the human feels a difference between the actual weight and the perceived weight of the object. We included this hypothesis in the robot dynamics. We then discussed the suitability of force control for the robot for lifting objects and developed several weight-perception-based force control strategies. These force control strategies may be compared to previously developed position control strategies, and the comparison results may help determine appropriate control for the robot to achieve desired maneuverability. The results, as a whole, may help develop human-friendly power assist devices to handle heavy objects in various industries.

scholarly journals Conventional and Fuzzy Force Control in Robotised Machining

2013 ◽  
Vol 210 ◽  
pp. 178-185 ◽  
Author(s):  
Zenon Hendzel ◽  
Andrzej Burghardt ◽  
Piotr Gierlak ◽  
Marcin Szuster
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Force Control ◽  
Closed Loop ◽  
Position Control ◽  
Control Strategies ◽  
Closed Loop System ◽  
Fuzzy Logic Systems ◽  
Complex Control System ◽  
Artificial Neural

This article presents an application of the hybrid position-force control of the robotic manipulator with use of artificial neural networks and fuzzy logic systems in complex control system. The mathematical description of the manipulator and a closed-loop system are presented. In the position control were used the PD controller and artificial neural networks, which compensate nonlinearities of the manipulator. The paper presents mainly the application of various strategies of the force control. The force control strategies using conventional controllers P, PI, PD, PID and fuzzy controllers are presented and discussed. All of the control methods were verified on the real object in order to make a comparison of a control quality.

scholarly journals Weight and social comparison: Does the weight of a stranger affect a person’s perception of their own weight?

2018 ◽  
Vol 5 (2) ◽  
pp. 205510291881926 ◽  
Author(s):  
Vibeke Tornhøj Christensen ◽  
Mads Meier Jæger
Keyword(s):  
Body Mass Index ◽  
Social Context ◽  
Social Comparison ◽  
Body Mass ◽  
Weight Perception ◽  
Future Research ◽  
Face To Face ◽  
Weight Level ◽  
Comparison Results ◽  
Perceived Weight

Research suggests that social context affects individuals’ perception of their own weight. Using face-to-face interviews as the social context, we analyze the effect of interviewers’ (N = 90) body mass index on respondents’ (N = 3068) self-perceived weight level. Respondents reported a higher weight level when the interviewer had a higher body mass index (absolute social comparison). Female respondents reported a lower weight level if interviewers had a higher body mass index than they did (relative social comparison). Results suggest that weight perception reflects both absolute and relative social comparison, especially among women. Future research should consider causation and self-selection when studying social context and body image.

Weight perception and symptoms of depression in rural Australian adolescents

2021 ◽  
pp. 103985622110092
Author(s):  
Samuel Skidmore ◽  
Catherine Hawke ◽  
Georgina Luscombe ◽  
Philip Hazell ◽  
Katharine Steinbeck
Keyword(s):  
Demographic Data ◽  
Weight Perception ◽  
World Health ◽  
Actual Weight ◽  
Symptoms Of Depression ◽  
Measured Weight ◽  
Illness Prevention ◽  
Perceived Weight ◽  
And Gender ◽  
Rural Adolescent

Objective To investigate associations between measured and perceived weight, and symptoms of depression in rural Australian adolescents. Method: At baseline a prospective rural adolescent cohort study collected demographic data, measured weight and height, weight self-perception, and presence of depression (Short Mood and Feelings Questionnaire). Using World Health Organisation’s (WHO) age and gender body mass index (BMI) standardisations, participants were classified into four perceptual groups: PG1 healthy/perceived healthy; PG2 overweight/perceived overweight; PG3 healthy/perceived overweight; and PG4 overweight/perceived healthy. Logistic regression analyses explored relationships between these groups and symptoms of depression. Results: Data on adolescents ( n = 339) aged 9–14. PG1 contained 63% of participants, PG2 18%, PG3 4% and PG4 14%. Across the cohort, 32% were overweight and 13% had symptoms of depression. PG2 (overweight/perceived overweight) were more likely to experience symptoms of depression than PG1 (healthy/perceived healthy; Adjusted Odds Ratio [AOR] 3.1, 95% CI 1.5–6.7). Females in PG3 (healthy/perceived overweight) were more likely to experience symptoms of depression (38%) than males (14%) and females in PG1 (10%, AOR 5.4, 95% CI 1.1–28.2). Conclusions: Results suggest that perceptions of being overweight may be a greater predictor for symptoms of depression than actual weight. This has public health implications for youth mental health screening and illness prevention.

scholarly journals Machine Learning-Based Cognitive Position and Force Controls for Power-Assisted Human–Robot Collaborative Manipulation

Machines ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 28
Author(s):  
S. M. Mizanoor Rahman
Keyword(s):  
System Performance ◽  
Position Control ◽  
Autonomous Robots ◽  
Control Strategies ◽  
Weight Perception ◽  
Health And Safety ◽  
Robotic System ◽  
Control Methods ◽  
Robot Systems ◽  
Adaptive Control Algorithm

Manipulation of heavy objects in industries is very necessary, but manual manipulation is tedious, adversely affects a worker’s health and safety, and reduces efficiency. On the contrary, autonomous robots are not flexible to manipulate heavy objects. Hence, we proposed human–robot systems, such as power assist systems, to manipulate heavy objects in industries. Again, the selection of appropriate control methods as well as inclusion of human factors in the controls is important to make the systems human friendly. However, existing power assist systems do not address these issues properly. Hence, we present a 1-DoF (degree of freedom) testbed power assist robotic system for lifting different objects. We also included a human factor, such as weight perception (a cognitive cue), in the robotic system dynamics and derived several position and force control strategies/methods for the system based on the human-centric dynamics. We developed a reinforcement learning method to predict the control parameters producing the best/optimal control performance. We also derived a novel adaptive control algorithm based on human characteristics. We experimentally evaluated those control methods and compared the system performance between the control methods. Results showed that both position and force controls produced satisfactory performance, but the position control produced significantly better performance than the force controls. We then proposed using the results to design control methods for power assist robotic systems for handling large and heavy materials and objects in various industries, which may improve human–robot interactions (HRIs) and system performance.

scholarly journals Neural Network Based Contact Force Control Algorithm for Walking Robots

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 287
Author(s):  
Byeongjin Kim ◽  
Soohyun Kim
Keyword(s):  
Neural Network ◽  
Contact Force ◽  
Force Control ◽  
Control Algorithm ◽  
Position Control ◽  
Linear Quadratic Regulator ◽  
Walking Robots ◽  
Test Model ◽  
Linear Quadratic ◽  
Contact Force Control

Walking algorithms using push-off improve moving efficiency and disturbance rejection performance. However, the algorithm based on classical contact force control requires an exact model or a Force/Torque sensor. This paper proposes a novel contact force control algorithm based on neural networks. The proposed model is adapted to a linear quadratic regulator for position control and balance. The results demonstrate that this neural network-based model can accurately generate force and effectively reduce errors without requiring a sensor. The effectiveness of the algorithm is assessed with the realistic test model. Compared to the Jacobian-based calculation, our algorithm significantly improves the accuracy of the force control. One step simulation was used to analyze the robustness of the algorithm. In summary, this walking control algorithm generates a push-off force with precision and enables it to reject disturbance rapidly.

A Survey on Brachiation Robots: An Energy-Based Review

Robotica ◽  
2021 ◽  
pp. 1-13
Author(s):  
Sibyla Andreuchetti ◽  
Vinícius M. Oliveira ◽  
Toshio Fukuda
Keyword(s):  
Special Class ◽  
Control Strategies ◽  
Underactuated Systems ◽  
Decision Variable ◽  
Robot Dynamics ◽  
Technical Literature ◽  
Control Schemes

SUMMARY Many different control schemes have been proposed in the technical literature to control the special class of underactuated systems, the- so-called brachiation robots. However, most of these schemes are limited with regard to the method by which the robot executes the brachiation movement. Moreover, many of these control strategies do not take into account the energy of the system as a decision variable. To observe the behavior of the system’s, energy is very important for a better understanding of the robot dynamics while performing the motion. This paper discusses a variety of energy-based strategies to better understand how the system’s energy may influence the type of motion (under-swing or overhand) the robot should perform.

Energy consumption optimization for AC drives position control

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Branislav Ftorek ◽  
Milan Saga ◽  
Pavol Orsansky ◽  
Jan Vittek ◽  
Peter Butko
Keyword(s):  
Control System ◽  
Energy Saving ◽  
Energy Savings ◽  
Position Control ◽  
Control Strategies ◽  
Content Type ◽  
Ac Drives ◽  
Energy Demands ◽  
Wide Range ◽  
Energy Consumption Optimization

Purpose The main purpose of this paper is to evaluate the two energy saving position control strategies for AC drives valid for a wide range of boundary conditions including an analysis of their energy expenses. Design/methodology/approach For energy demands analysis, the optimal energy control based on mechanical and electrical losses minimization is compared with the near-optimal one based on symmetrical trapezoidal speed profile. Both control strategies respect prescribed maneuver time and define acceleration profile for preplanned rest-to-rest maneuver. Findings Presented simulations confirm lower total energy expenditures of energy optimal control if compared with near-optimal one, but the differences are only small due to the fact that two energy saving strategies are compared. Research limitations/implications Developed overall control system consisting of energy saving profile generator, pre-compensator and position control system respecting principles of field-oriented control is capable to track precomputed state variables precisely. Practical implications Energy demands of both control strategies are verified and compared to simulations and preliminary experiments. The possibilities of energy savings were confirmed for both control strategies. Originality/value Experimental verification of designed control structure is sufficiently promising and confirmed assumed energy savings.

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