Robot Control Parameters Research Articles

Multi-objective optimization design method for the dimensions and control parameters of curling hexapod robot based on application performance

The curling hexapod robot is different from previous curling robots, which can mimic the throwing actions of curlers. To ensure its performance, the size and control parameters of the robot need to be optimized. However, there is still a lack of parameter design optimization methods for hexapod curling robots based on application performance and considering the coupling effects of size and control parameters. To meet this demand, this paper combines Simscape Multibody simulation modeling and genetic algorithm to propose a multi-objective optimization method for the dimensions and control parameters of curling hexapod robot based on application performance. This method uses Simscape Multibody to establish a parametric model of the curling robot, and proposes an optimization search method using genetic algorithms based on constraints and objective functions. Finally, the dimensions and control parameters of the curling robot that meet the application performance requirements are solved through the optimization method.

Research on force/position switching control of servo actuator for hydraulically driven joint robot

According to the robot’s walking motion characteristics, the position/force switching control is studied to realize the segmental control of the robot stroke. This stroke is controlled by position when the foot end of the robot descends from the suspension to the ground. To avoid excessive contact force when the robot touches the ground, force control is carried out when the foot touches the ground. Due to the force and position control methods and control parameters of the hydraulic quadruped robots are different, the precise mathematical model for the joint position control and joint force control of the leg joints of the hydraulic quadruped robot is established using the system identification method. A fuzzy multi-model switching algorithm is proposed to solve the problem of jumping and jitter of system parameters in the process of force/position switching. Through simulation and prototype experiments, fuzzy multi-model switching is compared with direct switching and multi-model switching, and the switching effect of the algorithm is verified.

Simulation Aided Co-Design for Robust Robot Optimization

This letter outlines a bi-level algorithm to concurrently optimize robot hardware and control parameters in order to minimize energy consumption during the execution of tasks and to ensure robust performance. The outer loop consists in a genetic algorithm that optimizes the co-design variables according to the system average performance when tracking a locally optimal trajectory in perturbed simulations. The tracking controller exploits the locally optimal feedback gains computed in the inner loop with a Differential Dynamic Programming algorithm, which finds the optimal reference trajectories. Our simulations feature a complete actuation model, including friction compensation and bandwidth limits. This strategy can potentially account for arbitrary perturbations, and discards solutions that cannot robustly meet the task requirements. The results show improved performance of the designed platform in realistic application scenarios, autonomously leading to the selection of lightweight and more transparent hardware.

Globally Optimal Solution to Inverse Kinematics of 7DOF Serial Manipulator

The Inverse Kinematics (IK) problem is concerned with finding robot control parameters to bring the robot into a desired position under the kinematics and joint limit constraints. We present a globally optimal solution to the IK problem for a general serial 7DOF manipulator with revolute joints and a polynomial objective function. We show that the kinematic constraints due to rotations can be all generated by the second-degree polynomials. This is an important result since it significantly simplifies the further step where we find the optimal solution by Lasserre relaxations of nonconvex polynomial systems. We demonstrate that the second relaxation is sufficient to solve a general 7DOF IK problem. Our approach is certifiably globally optimal. We demonstrate the method on the 7DOF KUKA LBR IIWA manipulator and show that we are, in practice, able to compute the optimal IK or certify infeasibility in 99.9% tested poses. We also demonstrate that by the same approach, we are able to solve the IK problem for any generic (random) manipulator with seven revolute joints.

Similarity Evaluation Rule and Motion Posture Optimization for a Manta Ray Robot

The current development of manta ray robots is usually based on functional bionics, and there is a lack of bionic research to enhance the similarity of motion posture. To better exploit the characteristics of bionic, a similarity evaluation rule is constructed herein by a Dynamic Time Warping (DTW) algorithm to guide the optimization of the control parameters of a manta ray robot. The Central Pattern Generator (CPG) network with time and space asymmetry oscillation characteristics is improved to generate coordinated motion control signals for the robot. To optimize similarity, the CPG network is optimized with the genetic algorithm and particle swarm optimization (GAPSO) to solve the problems of multiple parameters, high non-linearity, and uncertain parameter coupling in the CPG network. The experimental results indicate that the similarity between the forward motion pose of the optimized manta ray robot and the manta ray is improved to 88.53%.

Research on mechanism configuration and dynamic characteristics for multi-split transmission line mobile robot

PurposeThe purpose of this paper is to achieve the optimal system design of a four-wheel mobile robot on transmission line maintenance, as the authors know transmission line mobile robot is a kind of special robot which runs on high-voltage cable to replace or assist manual power maintenance operation. In the process of live working, the manipulator, working end effector and the working environment are located in the narrow space and with heterogeneous shapes, the robot collision-free obstacle avoidance movement is the premise to complete the operation task. In the simultaneous operation, the mechanical properties between the manipulator effector and the operation object are the key to improve the operation reliability. These put forward higher requirements for the mechanical configuration and dynamic characteristics of the robot, and this is the purpose of the manuscript.Design/methodology/approachBased on the above, aiming at the task of tightening the tension clamp for the four-split transmission lines, the paper proposed a four-wheel mobile robot mechanism configuration and its terminal tool which can adapt to the walking and operation on multi-split transmission lines. In the study, the dynamic models of the rigid robot and flexible transmission line are established, respectively, and the dynamic model of rigid-flexible coupling system is established on this basis, the working space and dynamic characteristics of the robot have been simulated in ADAMS and MATLAB.FindingsThe research results show that the mechanical configuration of this robot can complete the tightening operation of the four-split tension clamp bolts and the motion of robot each joint meets the requirements of driving torque in the operation process, which avoids the operation failure of the robot system caused by the insufficient or excessive driving force of the robot joint torque.Originality/valueFinally, the engineering practicability of the mechanical configuration and dynamic model proposed in the paper has been verified by the physical prototype. The originality value of the research is that it has double important theoretical significance and practical application value for the optimization of mechanical structure parameters and electrical control parameters of transmission line mobile robots.

Prediction of Passive Torque on Human Shoulder Joint Based on BPANN.

In upper limb rehabilitation training by exploiting robotic devices, the qualitative or quantitative assessment of human active effort is conducive to altering the robot control parameters to offer the patients appropriate assistance, which is considered an effective rehabilitation strategy termed as assist-as-needed. Since active effort of a patient is changeable for the conscious or unconscious behavior, it is considered to be more feasible to determine the distributions of the passive resistance of the patient's joints versus the joint angle in advance, which can be adopted to assess the active behavior of patients combined with the measurement of robotic sensors. However, the overintensive measurements can impose a burden on patients. Accordingly, a prediction method of shoulder joint passive torque based on a Backpropagation neural network (BPANN) was proposed in the present study to expand the passive torque distribution of the shoulder joint of a patient with less measurement data. The experiments recruiting three adult male subjects were conducted, and the results revealed that the BPANN exhibits high prediction accurate for each direction shoulder passive torque. The results revealed that the BPANN can learn the nonlinear relationship between the passive torque and the position of the shoulder joint and can make an accurate prediction without the need to build a force distribution function in advance, making it possible to draw up an assist-as-needed strategy with high accuracy while reducing the measurement burden of patients and physiotherapists.

Gaussian process inference modelling of dynamic robot control for expressive piano playing.

Piano is a complex instrument, which humans learn to play after many years of practice. This paper investigates the complex dynamics of the embodied interactions between a human and piano, in order to gain insights into the nature of humans' physical dexterity and adaptability. In this context, the dynamic interactions become particularly crucial for delicate expressions, often present in advanced music pieces, which is the main focus of this paper. This paper hypothesises that the relationship between motor control for key-pressing and the generated sound is a manifold problem, with high-degrees of non-linearity in nature. We employ a minimalistic experimental platform based on a robotic arm equipped with a single elastic finger in order to systematically investigate the motor control and resulting outcome of piano sounds. The robot was programmed to run 3125 key-presses on a physical digital piano with varied control parameters. The obtained data was applied to a Gaussian Process (GP) inference modelling method, to train a network in terms of 10 playing styles, corresponding to different expressions generated by a Musical Instrument Digital Interface (MIDI). By analysing the robot control parameters and the output sounds, the relationship was confirmed to be highly nonlinear, especially when the rich expressions (such as a broad range of sound dynamics) were necessary. Furthermore this relationship was difficult and time consuming to learn with linear regression models, compared to the developed GP-based approach. The performance of the robot controller was also compared to that of an experienced human player. The analysis shows that the robot is able to generate sounds closer to humans' in some expressions, but requires additional investigations for others.

Robot control parameters auto-tuning in trajectory tracking applications

Autonomy is increasingly demanded to industrial manipulators. Robots have to be capable to regulate their behavior to different operational conditions, adapting to the specific task to be executed without requiring high time/resource-consuming human intervention. Achieving an automated tuning of the control parameters of a manipulator is still a challenging task, which involves modeling/identification of the robot dynamics. This usually results in an onerous procedure, both in terms of experimental and data-processing time. This paper addresses the problem of automated tuning of the manipulator controller for trajectory tracking, optimizing control parameters based on the specific trajectory to be executed. A Bayesian optimization algorithm is proposed to tune both the low-level controller parameters (i.e., the equivalent link-masses of the feedback linearizator and the feedforward controller) and the high-level controller parameters (i.e., the joint PID gains). The algorithm adapts the control parameters through a data-driven procedure, optimizing a user-defined trajectory-tracking cost. Safety constraints ensuring, e.g., closed-loop stability and bounds on the maximum joint position error are also included. The performance of proposed approach is demonstrated on a torque-controlled 7-degree-of-freedom FRANKA Emika robot manipulator. The 25 robot control parameters (i.e., 4 link-mass parameters and 21 PID gains) are tuned in less than 130 iterations, and comparable results with respect to the FRANKA Emika embedded position controller are achieved. In addition, the generalization capabilities of the proposed approach are shown exploiting the proper reference trajectory for the tuning of the control parameters.

Human-in-the-loop optimization of wearable robots to reduce the human metabolic energy cost in physical movements

Most designs of wearable robots are based on human biomechanical statistics, engineering experience or individual experiments. Despite great successes, few of them consider the human–robot integration and individual differences between users. Additionally, the design periods, cost and safety also need to be further improved. Learning from the natural driving mechanism of human body, we propose a general human-in-the-loop (HIL) optimization designing approach for this kind of wearable robots. Firstly, the human–robot coupling model of the personalized wearable robot and the human musculoskeletal model are established. Then, the Computed Muscle Control (CMC) tool embedded in software OpenSim and the Bayesian optimization used in machine learning are combined to find the optimal design scheme for the personalized wearable robots to reduce the human metabolic energy cost in specific physical movement. The HIL approach could not only optimize the control parameters of wearable robots, but also optimize their geometry, material and any other design parameters flexibly and effectively. An application example for the HIL approach is also provided to help designers better understand and use the HIL method proposed in this paper.

Two-Stage Robot Controller Auto-Tuning Methodology for Trajectory Tracking Applications

Autonomy is increasingly demanded of industrial manipulators. Robots have to be capable of regulating their behavior to different operational conditions, without requiring high time/resource-consuming human intervention. Achieving an automated tuning of the control parameters of a manipulator is still a challenging task. This paper addresses the problem of automated tuning of the manipulator controller for trajectory tracking. A Bayesian optimization algorithm is proposed to tune firstly the low-level controller parameters (i.e., robot dynamics compensation), then the high-level controller parameters (i.e., the joint PID gains), providing a two-stage robot controller auto-tuning methodology. In both the optimization phases, the algorithm adapts the control parameters through a data-driven procedure, optimizing a user-defined trajectory tracking cost. Safety constraints ensuring, e.g., closed-loop stability and bounds on the maximum joint position errors, are also included. The performance of the proposed approach is demonstrated on a torque-controlled 7-degree-of-freedom FRANKA Emika robot manipulator. The 4 robot dynamics parameters (i.e., 4 link-mass parameters) are tuned in 40 iterations, while the robot control parameters (i.e., 21 PID gains) are tuned in 90 iterations. Comparable trajectory tracking-errors results with respect to the FRANKA Emika embedded position controller are achieved.

Unfastening of Hexagonal Headed Screws by a Collaborative Robot

Disassembly is a core procedure in remanufacturing. Disassembly is currently carried out mainly by human operators. It is important to reduce the labor content of disassembly through automation, to make remanufacturing more economically attractive. Threaded fastener removal is one of the most difficult disassembly tasks to be fully automated. This article presents a new method developed for automating the unfastening of screws. An electric nutrunner spindle with a geared offset adapter was fitted to the end of a collaborative robot. The position of a hexagonal headed screw in a fitted stage was known only approximately, and its orientation in the hole was unknown. The robot was programed to perform a spiral search motion to engage the tool onto the screw. A control strategy combining torque and position monitoring with active compliance was implemented. An existing robot cell was modified and utilized to demonstrate the concept and to assess the feasibility of the solution using a turbocharger as a disassembly case study. Note to Practitioners —Remanufacturing is known to generate substantial economic, social, and environmental benefits. Disassembly is the first operation in a remanufacturing process chain. Unfastening threaded parts (“unscrewing”) is a common disassembly task accounting for approximately 40% of all disassembly activity. Like other disassembly tasks, often, unscrewing has to be carried out manually in remanufacturing due to difficulties caused by the variable and unpredictable condition of the end-of-life (EoL) products to be remanufactured. Automating unscrewing operations should reduce the labor content of disassembly, thus lowering remanufacturing costs and promoting the adoption of remanufacturing. This article proposes the use of a collaborative robot to perform autonomous unfastening of hexagonal headed screws. Collaborative robots have built-in force sensors and can be programed to carry out operations involving not only position but also active force and compliance control. They can work safely alongside human operators, enabling the latter to focus on jobs requiring high cognitive or manipulation abilities. The article presents a novel spiral search technique developed to improve the rate of successful engagement between the robot end effector and the screw heads despite uncertainties in the location of the screws. The technique was successfully demonstrated on the dismantling of a turbocharger but can readily be applied to other EoL products with hexagonal headed screws. It can also be used with other kinds of screws (e.g., Phillips screws and slotted-head screws) simply by changing the tool and tuning the robot control parameters. A limitation of the proposed technique is that it can only deal reliably with undamaged screws. In our future research, we will consider screws that are in imperfect conditions through usage and develop appropriate solutions for their removal by robots.

O-08 Brain connectivity modulation after exoskeleton gait in chronic stroke survivors: a pilot study

Background Restoration of independent gait after stroke is a principal goal of survivors. Exoskeleton overground gait training provides trunk support and inter-limb coordination based on fine-tuning of the robot control parameters. The aim of our study was to identify short-term plasticity based on electroencephalography (EEG) data induced by a single trial of exoskeleton gait training in unilateral hemiplegic stroke survivors. Material and methods 64-channel EEG was recorded before gait (T0), after overground walking (T1) and after overground exoskeleton (EksoGT) gait training (T2) in 9 stroke survivors. Relative power and coherence were estimated for alpha1 (8–10 Hz), alpha2 (10.5–12.5 Hz) and beta (13–30 Hz) frequency ranges. Graph analysis assessed network model properties: node strength (S) and betweenness centrality (BC). Results Left-hemisphere stroke subjects showed S decrease over anterior and posterior cortex in alpha1, alpha2 and beta at T1 and T2. BC decreased at vertex at T1 and T2 in alpha2 and beta. Right-hemisphere stroke subjects displayed power decrease after exoskeleton walking in alpha2 over CPZ and CP2, and in beta over left contralateral sensorimotor cortex (cSM) and right ipsilateral prefrontal cortex (iPFC). S increased in alpha1 and alpha2 over cSM and iPFC at T2; BC decreased in alpha2 over iPFC at T2 compared to T1. Conclusions A single session of tailored exoskeleton training provides short-term neuroplastic modulation in chronic stroke. Right-hemisphere stroke subjects re-establish a more physiological activation network; a similar reorganization does not take place in left-hemisphere stroke subjects, supporting the role of lateralization in limb control and stroke recovery.

МЕТОД АВТОКАЛИБРОВКИ ПАРАМЕТРОВ УПРАВЛЕНИЯ УЧЕБНЫМ РОБОТОМ С ПОМОЩЬЮ БИБЛИОТЕКИ МАШИННОГО ЗРЕНИЯ OPENCV

МЕТОД АВТОКАЛИБРОВКИ ПАРАМЕТРОВ УПРАВЛЕНИЯ УЧЕБНЫМ РОБОТОМ С ПОМОЩЬЮ БИБЛИОТЕКИ МАШИННОГО ЗРЕНИЯ OPENCV

Reprint of “Multimodal adaptive interfaces for 3D robot-mediated upper limb neuro-rehabilitation: An overview of bio-cooperative systems”

Robot-mediated neuro-rehabilitation has been proved to be an effective therapeutic approach for upper limb motor recovery after stroke, though its actual potential when compared to other conventional approaches has still to be fully demonstrated. Most of the proposed solutions use a planar workspace. One key aspect for influencing motor recovery mechanisms, such as neuroplasticity and the level of motivation and involvement of the patient in the exercise, is the design of patient-tailored protocols and on-line adaptation of the assistance provided by the robotic agent to the patient performance. Also, when abilities for performing activities of daily living shall be targeted, exercises in 3D workspace are highly preferable. This paper wants to provide a complete overview on bio-cooperative systems on neuro-rehabilitation, with a special focus on 3D multimodal adaptive interfaces, by partly in-depth reviewing the literature and partly proposing an illustrative case of how to build such a bio-cooperative based on the authors’ current research. It consists of an operational robotic platform for 3D upper limb robot-aided rehabilitation, directly derived from the MAAT system previously developed by the same research group. The system features on-line adaptation of therapy characteristics to specific patient needs and to the measured level of performance, by including the patient in the control loop. The system is composed of a 7-DoF robot arm, an adaptive interaction control system, a motorized arm-weight support system and a module for on-line evaluation of patient performance. Such module records patient biomechanical data through an unobtrusive, wearable sensory system, evaluates patient biomechanical state and updates robot control parameters for modifying level of assistance and task complexity in the 3D workspace. In addition, a multimodal interface provides information needed to control the motorized arm-weight support by means of a dedicated cable–pulley system.

Multimodal adaptive interfaces for 3D robot-mediated upper limb neuro-rehabilitation: An overview of bio-cooperative systems

Robot-mediated neuro-rehabilitation has been proved to be an effective therapeutic approach for upper limb motor recovery after stroke, though its actual potential when compared to other conventional approaches has still to be fully demonstrated. Most of the proposed solutions use a planar workspace. One key aspect for influencing motor recovery mechanisms, such as neuroplasticity and the level of motivation and involvement of the patient in the exercise, is the design of patient-tailored protocols and on-line adaptation of the assistance provided by the robotic agent to the patient performance. Also, when abilities for performing activities of daily living shall be targeted, exercises in 3D workspace are highly preferable. This paper wants to provide a complete overview on bio-cooperative systems on neuro-rehabilitation, with a special focus on 3D multimodal adaptive interfaces, by partly in-depth reviewing the literature and partly proposing an illustrative case of how to build such a bio-cooperative based on the authors’ current research. It consists of an operational robotic platform for 3D upper limb robot-aided rehabilitation, directly derived from the MAAT system previously developed by the same research group. The system features on-line adaptation of therapy characteristics to specific patient needs and to the measured level of performance, by including the patient in the control loop. The system is composed of a 7-DoF robot arm, an adaptive interaction control system, a motorized arm-weight support system and a module for on-line evaluation of patient performance. Such module records patient biomechanical data through an unobtrusive, wearable sensory system, evaluates patient biomechanical state and updates robot control parameters for modifying level of assistance and task complexity in the 3D workspace. In addition, a multimodal interface provides information needed to control the motorized arm-weight support by means of a dedicated cable–pulley system.

An Integrated Decision Making Approach for Adaptive Shared Control of Mobility Assistance Robots

Mobility assistance robots provide support to elderly or patients during walking. The design of a safe and intuitive assistance behavior is one of the major challenges in this context. We present an integrated approach for the context-specific, on-line adaptation of the assistance level of a rollator-type mobility assistance robot by gain-scheduling of low-level robot control parameters. A human-inspired decision-making model, the drift-diffusion Model, is introduced as the key principle to gain-schedule parameters and with this to adapt the provided robot assistance in order to achieve a human-like assistive behavior. The mobility assistance robot is designed to provide (a) cognitive assistance to help the user following a desired path towards a predefined destination as well as (b) sensorial assistance to avoid collisions with obstacles while allowing for an intentional approach of them. Further, the robot observes the user long-term performance and fatigue to adapt the overall level of (c) physical assistance provided. For each type of assistance a decision-making problem is formulated that affects different low-level control parameters. The effectiveness of the proposed approach is demonstrated in technical validation experiments. Moreover, the proposed approach is evaluated in a user study with 35 elderly persons. Obtained results indicate that the proposed gain-scheduling technique incorporating ideas of human decision-making models shows a general high potential for the application in adaptive shared control of mobility assistance robots.

Layout of robot cells based on kinematic constraints

This article presents a new approach in determining machine layout in robotic cells and the corresponding feasible robot configuration at each machine worksite, such that the total cycle time is optimised. Simultaneously, the proposed method considers many design features required to develop a practical workcell including machine dimensions, worksites, orientations, and operational characteristics of the deployed robot. A realistic approach is presented to model the problem using the robot joint space. Such method will enable the robot control parameters to be considered in the problem formulation. A non-linear optimisation model is presented and solved using the Sequential Quadratic Programming algorithm available in the Matlab® optimisation tool box. By implementing to a real industrial robot, the feasibility of the proposed approach is examined through several examples. The simulation test results showed that the proposed approach based on cycle time optimisation produced better results than previous methods developed using constant joint velocity. A 3D simulation software is used to provide a practical analysis of the developed layout and robot dynamics.

Intelligent Control for a Robot Belt Grinding System

A robot belt grinding system provides promising prospects for relieving hand grinders from their noisy work environment, as well as for improving machining accuracy and product consistency. However, for a manufacturing system with a flexible grinder, controlling the robot to perform precise material removal from free-form surfaces is a challenge. In the belt grinding process, material removal is related to a variety of factors, such as workpiece shape, contact force, and robot velocity. Some factors of the grinding process, such as belt wear, are time-variant. To achieve the desired removal in the grinding process, an intelligent control method for the industrial robot is proposed in this paper. First, an adaptive grinding process model that can track discontinuous changes in working conditions is constructed to precisely predict material removal in accordance with in situ measurement data. With incorporated prior knowledge, the method considerably improves model accuracy, which worsens when new samples from an in situ measurement are insufficient or are unevenly distributed under new working conditions. After this, an online trajectory generation method for the robot control parameters is proposed. By calculating the optimal control parameters in real time, the control transition process is shortened and its negative effect on grinding quality is reduced. Finally, the preliminary grinding experiments validate the workability and effectiveness of the proposed control method.

An Adaptive Modeling Method for a Robot Belt Grinding Process

A robot belt grinding system has a good prospect for releasing hand grinders from their dirty and noisy work environment. However, as a kind of manufacturing system with a flexible grinder, it is a challenge to model its processes and control grinding removal precisely for free-formed surfaces. In the belt grinding process, material removal is related to a variety of factors, such as workpiece shape, contact force, and robot velocity. Some factors of the grinding process, such as belt wear, are time variant. In order to control material removal in the robot grinding process, an effective approach is to build a grinding process model that can track changes in the working condition and predict material removal precisely. In this paper, an adaptive modeling method based on statistic machine learning is proposed. The major idea is to build an initial model based on support vector regression using historical grinding data serving as training samples. Afterward, the trained model is modified according to in situ measurement data. Robot control parameters can then be calculated using the grinding process model. The results of the blade grinding experiments demonstrate that this approach is workable and effective.