Parameters Of Robot Research Articles

Human-in-the-loop layered architecture for control of a wearable ankle–foot robot

Intelligent wearable robotics is a promising approach for the development of devices that can interact with people and assist them in daily activities. This work presents a novel human-in-the-loop layered architecture to control a wearable robot while interacting with the human body. The proposed control architecture is composed of high-, mid- and low-level computational and control layers, together with wearable sensors, for the control of a wearable ankle–foot robot. The high-level layer uses Bayesian formulation and a competing accumulator model to estimate the human posture during the gait cycle. The mid-level layer implements a Finite State Machine (FSM) to prepare the control parameters for the wearable robot based on the decisions from the high-level layer. The low-level layer is responsible for the precise control of the wearable robot over time using a cascade proportional–integral–derivative (PID) control approach. The human-in-the-loop layered architecture is systematically validated with the control of a 3D printed wearable ankle–foot robot to assist the human foot while walking. The assistance is applied lifting up the human foot when the toe-off event is detected in the walking cycle, and the assistance is removed allowing the human foot to move down and contact the ground when the heel-contact event is detected. Overall, the experiments in offline and real-time modes, undertaken for the validation process, show the potential of the human-in-the-loop layered architecture to develop intelligent wearable robots capable of making decisions and responding fast and accurately based on the interaction with the human body.

Robot Zero-Moment Control Algorithm Based on Parameter Identification of Low-Speed Dynamic Balance

This paper proposes a zero-moment control torque compensation technique. After compensating the gravity and friction of the robot, it must overcome a small inertial force to move in compliance with the external force. The principle of torque balance was used to realise the zero-moment dragging and teaching function of the lightweight collaborative robot. The robot parameter identification based on the least square method was used to accurately identify the robot torque sensitivity and friction parameters. When the robot joint rotates at a low speed, it can approximately satisfy the torque balance equation. The experiment uses the joint position and the current motor value collected during the whole moving process under the low-speed dynamic balance as the excitation signal to realise the parameter identification. After the robot was compensated for gravity and static friction, more precise torque control was realised. The zero-moment dragging and teaching function of the robot was more flexible, and the drag process was smoother.

Mobilni robot za praćenje drveća parka - projektovanje i modeliranje

One of the modern problems in the field of ecology is the creation of environmentally friendly equipment for monitoring and maintaining trees in parks and forests. The traditional use of forest machines and self-propelled tractors with internal combustion engines has a negative impact on the environment as a result of pollutant emissions, i.e., combustion products and fuel residues. An alternative to this tradition can be the use of mobile robots with remote control of their electric drives when performing such technological operations as pruning bacterial growths of trees and diagnosing the state of tree massifs. The article proposes a fundamentally new mobile robot design for monitoring park trees. The main differences between the robot are the new designs of the body and the walking mechanisms of the mobile robot. These design differences provide the robot with high maneuverability when choosing the path of movement along the tree trunk and reliable holding of the robot body on the tree at a sufficient movement speed to perform diagnostics of the state of tree massifs. The article also describes the dynamic models of the movement of a mobile robot along a tree trunk. It presents the simulation results in the form of graph-analytical dependencies of the robot parameters, which constitutes the scientific aspect of the problem. The main motivation of the conducted research is the creation of environmentally friendly equipment in the form of a mobile robot with a reliable system of retention on the surface moving and sufficient performance to perform park tree monitoring operations.

Implementation of an Embedded System into the Internet of Robotic Things.

The article describes the use of embedded systems in the Industrial Internet of Things and its benefits for industrial robots. For this purpose, the article presents a case study, which deals with an embedded system using an advanced microcontroller designed to be placed directly on the robot. The proposed system is being used to collect information about industrial robot parameters that impact its behavior and its long-term condition. The device measures the robot's surroundings parameters and its vibrations while working. Besides that, it also has an enormous potential to collect other parameters such as air pollution or humidity. The collected data are stored on the cloud platform and processed and analysed. The embedded system proposed in this article is conceived to be small and mobile, as it is a wireless system that can be easily applied to any industrial robot.

Design and Test of Duckbill Welding Robot for Cotton Seeder

To improve the automation, welding efficiency, and welding quality of duckbill welding of the cotton seeder, this study designed a cotton seeder duckbill welding robot. According to the characteristics of the duckbill weldment and welding requirements, the overall structure of the welding robot was determined, including the girdle feeding mechanism, static duckbill feeding mechanism, hinge feeding mechanism, welding fixture, welding actuator, and control system. To realize the continuous automatic feeding, positioning, fixing, welding, and unloading of the workpiece in the duckbill welding, the feeding mechanism adopts the method of cooperative cooperation of inductive proximity switch, electromagnet, and cylinder. The main body of the welding fixture adopts the pneumatic clamping method; the welding actuator adopts the synchronous belt module electric drive so that the welding torch can move in a straight line along the X axis and the Z axis. The welding process of the duckbill was simulated by Simufact Welding software, and the deformation and stress changes of the weldment were compared and analyzed when the single-sided single welding, the bilateral symmetrical double welding torch, two welding forms, and two welding process parameters were used to determine the welding process parameters of the welding robot. The prototype was made and the welding test was carried out. The test results show that the duckbill welding robot of the cotton seeder has stable feeding, solid clamping, accurate positioning, and high welding efficiency. According to the national standard, the appearance of the duckbill weld is inspected. The surface of the duckbill weld and the heat-affected zone has no cracks, incomplete fusion, slag inclusion, crater, and porosity. The forming quality of the welded parts is good. The design of the duckbill welding robot for cotton seeder is helpful in solving the problems of cumbersome positioning and clamping and low efficiency in manual and semi-automatic duckbill welding robots, which provides a strong guarantee for the large-scale and standardized welding production of the dibbler duckbill.

Adaptive Interaction Control of Compliant Robots Using Impedance Learning

This paper presents an impedance learning-based adaptive control strategy for series elastic actuator (SEA)-driven compliant robots without the measurement of the robot-environment interaction force. The adaptive controller is designed based on the command filter-based adaptive backstepping approach, where a command filter is used to decrease computational complexity and avoid the requirement of high derivatives of the robot position. In the controller, environmental impedance profiles and robotic parameter uncertainties are estimated using adaptive learning laws. Through a Lyapunov-based theoretical analysis, the tracking error and estimation errors are proven to be semiglobally uniformly ultimately bounded. The control effectiveness is illustrated through simulations on a compliant robot arm.

Reinforcement learning approach to the control of heavy material handling manipulators for agricultural robots

In this paper, we consider the optimal control problem of heavy material handling manipulators for agricultural robots. Unlike the existing results on agricultural robots, the robot parameters may be unknown for the designer in this paper. To learn the linear quadratic control gain under unknown robot parameters, two reinforcement learning algorithms, i.e., policy iteration (PI) algorithm and value iteration (VI) algorithm, are proposed. Then, through combining the advantages of PI algorithm and VI algorithm, i.e., satisfactory convergence rate and without the restriction on feasibility initial control policy, respectively, a hybrid iteration (HI) algorithm is proposed, which can both achieve a satisfactory convergence rate and remove restrictions on feasibility initial control policy. It is shown that the convergence of the proposed HI algorithm can be achieved in theory. Finally, a simulation example is given to show that our designed HI algorithm can achieve a satisfactory simulation time.

A distance calibration method for kinematic parameters of serial industrial robots considering the accuracy of relative position

This paper proposes a kinematic calibration method based on the optimized distance error function to improve the accuracy of the relative positions of industrial robots. For implementation, a three-dimensional measurement system with R-test is designed to measure the robot end-effector's distance, which is the first successful use of this system for robot calibration. Then, an optimization function of the distance errors is proposed without an approximation condition to identify the controller's modifiable parameter errors. In addition, the influence of the redundant parameter on the error of relative position is analyzed, which indicates it can further improve the relative position’s accuracy. Simulations prove the performance of the proposed solution by comparing it with the current distance and positioning error methods. The validation experiment shows the proposed method reduced the mean of distance error/distance and relative positioning error/relative positioning from 14.30 µm/mm to 4.4 µm/mm and from 20.15 µm/mm to 10.85 µm/mm.

Automatic Control of a Mobile Manipulator Robot Based on Type-2 Fuzzy Sliding Mode Technique

In this paper, an automatic control method based on type-2 fuzzy sliding mode control for a mobile arm robot is presented. These types of robots have very complex dynamics due to the uncertainty of the arm parameters and the mobility of their base, so conventional control methods do not provide a suitable solution. The proposed method proves convergence with Lyapunov theory, and its convergence is mathematically guaranteed. A type-2 fuzzy system is responsible for approximating unmodulated dynamics, nonlinear terms, and uncertain parameters. In simulations, the performance of the proposed method with different situations, including uncertainty in arm parameters, uncertainty in mobile robot parameters (arm robot base), uncertainty in load, as well as indeterminacy in modeling have been applied. The comparison with two conventional controllers shows the efficiency and superiority of the proposed method.

No-code robotic programming for agile production: A new markerless-approach for multimodal natural interaction in a human-robot collaboration context.

Industrial robots and cobots are widely deployed in most industrial sectors. However, robotic programming still needs a lot of time and effort in small batch sizes, and it demands specific expertise and special training, especially when various robotic platforms are required. Actual low-code or no-code robotic programming solutions are exorbitant and meager. This work proposes a novel approach for no-code robotic programming for end-users with adequate or no expertise in industrial robotic. The proposed method ensures intuitive and fast robotic programming by utilizing a finite state machine with three layers of natural interactions based on hand gesture, finger gesture, and voice recognition. The implemented system combines intelligent computer vision and voice control capabilities. Using a vision system, the human could transfer spatial information of a 3D point, lines, and trajectories using hand and finger gestures. The voice recognition system will assist the user in parametrizing robot parameters and interacting with the robot’s state machine. Furthermore, the proposed method will be validated and compared with state-of-the-art “Hand-Guiding” cobot devices within real-world experiments. The results obtained are auspicious, and indicate the capability of this novel approach for real-world deployment in an industrial context.

Meta Reinforcement Learning for Optimal Design of Legged Robots

The process of robot design is a complex task and the majority of design decisions are still based on human intuition or tedious manual tuning. A more informed way of facing this task is computational design methods where design parameters are concurrently optimized with corresponding controllers. Existing approaches, however, are strongly influenced by predefined control rules or motion templates and cannot provide end-to-end solutions. In this paper, we present a design optimization framework using model-free meta reinforcement learning, and its application to the optimizing kinematics and actuator parameters of quadrupedal robots. We use meta reinforcement learning to train a locomotion policy that can quickly adapt to different designs. This policy is used to evaluate each design instance during the design optimization. We demonstrate that the policy can control robots of different designs to track random velocity commands over various rough terrains. With controlled experiments, we show that the meta policy achieves close-to-optimal performance for each design instance after adaptation. Lastly, we compare our results against a model-based baseline and show that our approach allows higher performance while not being constrained by predefined motions or gait patterns.

Design and Closed‐Loop Motion Planning of an Untethered Swimming Soft Robot Using 2D Discrete Elastic Rods Simulations

Despite tremendous progress in the development of untethered soft robots in recent years, existing systems lack the mobility, model‐based control, and motion planning capabilities of their piecewise rigid counterparts. As in conventional robotic systems, the development of versatile locomotion of soft robots is aided by the integration of hardware design and control with modeling tools that account for their unique mechanics and environmental interactions. Here, a framework for physics‐based modeling, motion planning, and control of a fully untethered swimming soft robot is introduced. This framework enables offline co‐design in the simulation of robot parameters and gaits to produce effective open‐loop behaviors and enables closed‐loop planning over motion primitives for feedback control of a frog‐inspired soft robot testbed. This pipeline uses a discrete elastic rods (DERs) physics engine that discretizes the soft robot as many stretchable and bendable rods. On hardware, an untethered aquatic soft robot that performs frog‐like rowing behaviors is engineered. Hardware validation verifies that the simulation has sufficient accuracy to find the best candidates for sets of parameters offline. The simulator is then used to generate a trajectory library of the robot's motion in simulation that is used in real‐time closed‐loop path following experiments on hardware.

Predictive Control of the Mobile Robot under the Deep Long-Short Term Memory Neural Network Model.

At present, there is a phenomenon of network data packet loss in the trajectory tracking control system, which will degrade or even destabilize the system's performance. Therefore, this work first explains the theory of the deep long-short term memory (LSTM) neural network model, the kinematic model of mobile robots, and the trajectory tracking error model. The reasons for data packet loss in the control system are analyzed. Second, a prediction model based on the LSTM network is designed according to the theory mentioned above. Finally, the training effect of the LSTM model and the robot trajectory tracking effect based on the model are tested by setting up simulation experiments. The research results are as follows: (1) The pose test error of the mobile robot will eventually tend to zero through the simulation curve generated by the pose parameters (x, y, θ) of the mobile robot. (2) The trajectory tracking error of the deep LSTM neural network prediction and compensation method with the packet loss rate of 5% is less than that with the packet loss rate of 10%. (3) The linear velocity υ of the mobile robot based on the prediction model of the LSTM network varies greatly but is always in the interval (−2, 2). Its angular velocity ω initially fluctuates greatly but gradually tends to zero after about 13 s. (4) When the prediction model tracks the trajectory of the robot, the horizontal position x, the vertical position y, and the angle θ coincide with the reference trajectory. The exploration is conducted to provide a reference for the research on data packet loss in the networked mobile robot trajectory tracking system.

Rescue of the Walking Robot from an Emergency Position on the Back in the Presence of Uneven Support

A method of swinging of the six-legged robot to ensure its overturn from the "upside down" position is proposed. As a support, we consider an inclined plane with a slight slope towards the flip, with a pit and optionally with a bump next to it. The position of the fixed support can be set by sequential rotations around two different axes. It is shown that the overturn is possible with the help of cyclic movement of the legs, if the body has an upper shell in the form of a truncated cylinder. The legs on the pre-chosen edge of the body through which the flip should occur, are passive, and straightened along the body so that they do not interfere with the flip. The legs on the opposite edge are active; they perform synchronous movement in a plane perpendicular to the longitudinal axis of the body, with a fixed angle in the knee. The results of simulation of the full dynamics of the robot in contact with the support by means of the "Universal mechanism" software package are presented. Specifics of swinging due to the slope, the pit and the bump are shown. For a typical set of robot parameters, the results of numerical experiments for pits of approximately the largest admissible sizes in cases where the support surface is rotated around one axis, around two axes, and for different types of pits, deep and shallow. In all cases, there is a bump next to the pit.

Singularity Analysis and Geometric Optimization of a 6-DOF Parallel Robot for SILS

The paper presents the singularity analysis and the geometric optimization of a 6-DOF (Degrees of Freedom) parallel robot for SILS (Single-Incision Laparoscopic Surgery). Based on a defined set of input/output constraint equations, the singularities of the parallel robotic system are determined and geometrically interpreted. Then, the geometric parameters (e.g., the lengths of the mechanism links) for the 6-DOF parallel robot for SILS are optimized such that the robotic system complies with an operational workspace defined in correlation with the SILS task. A numerical analysis of the singularities showed that the operational workspace is singularity free. Furthermore, numerical simulations validate the parallel robot for the next developing stages (e.g., designing and prototyping stages).

Robot Workspace Optimization based on Monte Carlo Method and Multi Island Genetic Algorithm

Workspace analysis is an indispensable part of robot research. The workspace volume is an important factor to measure the working ability of parallel robot. In order to maximize the workspace of parallel robot, this paper solves the workspace of robot by using Monte Carlo method, and obtains that the workspace volume is 2.142×106 mm3, which is optimized by the multi Island genetic algorithm in the global optimization algorithm. After optimization, the workspace volume increases to 8.25×106 mm3, the volume of workspace before and after optimization is increased by 3.85 times. The influence of various structural parameters of the parallel robot on the workspace volume is analyzed and studied. It is obtained that the rod length ratio of the connecting rod and the driving rod has the greatest influence on the workspace volume, followed by the ratio of the center angle of the long and short sides, and the radius of the moving platform has the least influence. Furthermore, the influence of single parameter on the workspace volume is analyzed. When other parameters remain unchanged, the maximum workspace volume can be achieved when the rod length ratio of connecting rod and driving rod is 1.25, or the ratio of center angle of long and short sides is 1.53.

Evaluation of Parameter Identification of a Real Manipulator Robot

Given the widespread use of the Kalman filter in robotics, an increasing number of researchers devote themselves to its study and application. This work underscores the importance of this filter while analyzing the modifications made to the same to improve its performance and reduce its deficiencies in some fields and presenting some of its applications in robotics. The following methods are presented in this study: least squares (LS), Hopfield Neural Networks (HNN), Extended Kalman filter (EKF), and Unscented Kalman filter (UKF). These methods are used in the parameter identification of a Selective Compliant Assembly Robot Arm (SCARA) robot with 3-Degrees of Freedom (3-DoF) and a clamp at its end. The dynamic model of this robot is obtained and employed to identify its parameters; then, the identification results are compared considering the difference between the obtained parameters and the real values of the robot parameters; in this comparison, the good results yielded by the LS and UKF method stand out. Subsequently, the obtained parameters through each method are validated by measuring different performance indexes—during trajectory tracking—such as: Residual Mean Square Error (RMSE), Integral of the Absolute Error (IAE), and the Integral of the Square Error (ISE). In this way, a comparison of the robot’s performance is possible.

Online Kinematic Calibration for Legged Robots

This paper describes an online method to calibrate certain kinematic parameters of legged robots, including leg lengths, that can be difficult to measure offline due to dynamic deformation effects and rolling contacts. A kinematic model of the robot’s legs that depends on these parameters is used, along with measurements from joint encoders, foot contact sensors, and aninertial measurement unit (IMU) to predict the robot’s body velocity. This predicted velocity is then compared to another velocity measurement from, for example, a camera or motion capture system, and the difference between them is used to compute anupdate on the kinematic parameters. The method can be incorporated into both Kalman filter or sliding-window optimization-based state estimator. We provide a theoretical observability analysis of our method, as well as validation both in simulation and on hardware. Hardware experiments demonstrate that online kinematic calibration can significantly reduce position drift when relying on odometry.

Impedance Learning-Based Adaptive Control for Human–Robot Interaction

In this article, a new learning-based time-varying impedance controller is proposed and tested to facilitate an autonomous physical human–robot interaction (pHRI). Novel adaptation laws are formulated for online adjustment of robot impedance based on human behavior. Two other sets of update rules are defined for intelligent coping with the robot’s structured and unstructured uncertainties. These rules ensure stability via Lyapunov’s theorem and provide uniform ultimate boundedness (UUB) of the closed-loop system’s response, without a need for HRI force/torque measurement. Accordingly, the convergence of response signals, including errors in tracking, online impedance learning, robot parameter adaptation, and controller gain variation, is proven to operate in a bounded region (compact set) in the presence of robot and human uncertainties and bounded disturbances. The performance of the developed intelligent impedance-varying control strategy is investigated through comprehensive experimental studies in a repetitive following task with a moving target.

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%.