Field Of Robot Control Research Articles

Using HSV-based approach for detecting and grasping an object by the industrial mechatronic system

In term of the industrialization era, robot gradually appears in some production stages instead of worker. There is an irreversible tendency to deploy the image processing techniques into the field of robot control. In recent years, the vision-based techniques have reached to many considerable achievements. Most of them requires the complicated setup, specialized camera and professional operator for the process of burden computation. In this paper, an efficient solution based on the vision technique to detect and grasp an object in the indoor environment is introduced. The framework of this system which contains the geometrical constraints, theories of robot control and hardware platform, is described. To manipulate the task of detecting and grasping, the proposed method from calibration to visual estimation is mentioned in detail. From the results of both theoretical simulation and experiment, it is clearly stated that our approach is efficient, feasible and applicable.

Sterowanie autonomicznym bezzałogowym statkiem powietrznym z wykorzystaniem uczenia przez wzmacnianie

Reinforcement learning is of increasing importance in the field of robot control and simulation plays a key role in this process. In the unmanned aerial vehicles (UAVs, drones), there is also an increase in the number of published scientific papers involving this approach. In this work, an autonomous drone control system was prepared to fly forward (according to its coordinates system) and pass the trees encountered in the forest based on the data from a rotating LiDAR sensor. The Proximal Policy Optimization (PPO) algorithm, an example of reinforcement learning (RL), was used to prepare it. A custom simulator in the Python language was developed for this purpose. The Gazebo environment, integrated with the Robot Operating System (ROS), was also used to test the resulting control algorithm. Finally, the prepared solution was implemented in the Nvidia Jetson Nano eGPU and verified in the real tests scenarios. During them, the drone successfully completed the set task and was able to repeatable avoid trees and fly through the forest.

Cross-modal self-attention mechanism for controlling robot volleyball motion.

The emergence of cross-modal perception and deep learning technologies has had a profound impact on modern robotics. This study focuses on the application of these technologies in the field of robot control, specifically in the context of volleyball tasks. The primary objective is to achieve precise control of robots in volleyball tasks by effectively integrating information from different sensors using a cross-modal self-attention mechanism. Our approach involves the utilization of a cross-modal self-attention mechanism to integrate information from various sensors, providing robots with a more comprehensive scene perception in volleyball scenarios. To enhance the diversity and practicality of robot training, we employ Generative Adversarial Networks (GANs) to synthesize realistic volleyball scenarios. Furthermore, we leverage transfer learning to incorporate knowledge from other sports datasets, enriching the process of skill acquisition for robots. To validate the feasibility of our approach, we conducted experiments where we simulated robot volleyball scenarios using multiple volleyball-related datasets. We measured various quantitative metrics, including accuracy, recall, precision, and F1 score. The experimental results indicate a significant enhancement in the performance of our approach in robot volleyball tasks. The outcomes of this study offer valuable insights into the application of multi-modal perception and deep learning in the field of sports robotics. By effectively integrating information from different sensors and incorporating synthetic data through GANs and transfer learning, our approach demonstrates improved robot performance in volleyball tasks. These findings not only advance the field of robotics but also open up new possibilities for human-robot collaboration in sports and athletic performance improvement. This research paves the way for further exploration of advanced technologies in sports robotics, benefiting both the scientific community and athletes seeking performance enhancement through robotic assistance.

SNS-Toolbox: An Open Source Tool for Designing Synthetic Nervous Systems and Interfacing Them with Cyber-Physical Systems.

One developing approach for robotic control is the use of networks of dynamic neurons connected with conductance-based synapses, also known as Synthetic Nervous Systems (SNS). These networks are often developed using cyclic topologies and heterogeneous mixtures of spiking and non-spiking neurons, which is a difficult proposition for existing neural simulation software. Most solutions apply to either one of two extremes, the detailed multi-compartment neural models in small networks, and the large-scale networks of greatly simplified neural models. In this work, we present our open-source Python package SNS-Toolbox, which is capable of simulating hundreds to thousands of spiking and non-spiking neurons in real-time or faster on consumer-grade computer hardware. We describe the neural and synaptic models supported by SNS-Toolbox, and provide performance on multiple software and hardware backends, including GPUs and embedded computing platforms. We also showcase two examples using the software, one for controlling a simulated limb with muscles in the physics simulator Mujoco, and another for a mobile robot using ROS. We hope that the availability of this software will reduce the barrier to entry when designing SNS networks, and will increase the prevalence of SNS networks in the field of robotic control.

Mastering the Complex Assembly Task With a Dual-Arm Robot: A Novel Reinforcement Learning Method

Deep reinforcement learning (DRL) has achieved great success across multiple fields; however, in the field of robot control, the acquisition of large amounts of motion data from real robots is challenging. In this work, an algorithm is proposed to train a neural network model with a large amount of data in a simulated environment and then transfer the model to the real environment. Proximal policy optimization (PPO) is used to train the agent in simulator, and generative adversarial imitation learning (GAIL) is specified to transfer the model to the real world. The algorithm can guide the two-armed robot to complete the task well in the face of complex assembly tasks. A total of three tasks of different difficulties were set to test the performance of the algorithm. In a large experimental study, the proposed algorithm outperforms other algorithms, and the real robot arm completes the assembly task significantly faster than script and keyboard operations.

Error-Summation Enhanced Newton Algorithm for Model Predictive Control of Redundant Manipulators

Redundant manipulators have been investigated and employed in various fields, and its trajectory tracking is of much importance in the field of robotic control. In this article, a model predictive control (MPC) scheme for the trajectory tracking of redundant manipulators is constructed, which minimizes the tracking error, velocity norm, and acceleration norm simultaneously. The commonly used trajectory tracking schemes for redundant manipulators, such as the minimum-velocity-norm scheme and minimum-acceleration-norm scheme, handle joint limits at different levels by introducing additional parameters, which reduces the feasible region of decision variables. In contrast, the proposed scheme directly considers these limits at three different levels, without reducing the feasible region of decision variables. In addition, to compensate for the deficiencies of most existing algorithms in noise environments, an error-summation enhanced Newton (ESEN) algorithm is proposed for solving the MPC scheme. Through theoretical analysis, it is determined that the proposed ESEN algorithm has a small steady-state error under noise conditions. Finally, in contrast with the comparative trajectory tracking schemes, as determined through computer simulations and experiments, the proposed MPC scheme solved by the ESEN algorithm not only enables the redundant manipulator to perform the trajectory tracking task in excellent fashion, but also offers advantages of high efficiency, fast responsiveness, and strong noise tolerance.

Magnetic Self-Assembled Pearl Necklace-like Microstructure for Improving the Performance of a Flexible Strain Sensor

Flexible strain sensors have important applications in wearable devices, electronic skin, robotics, and virtual reality. However, simple and effective methods to adjust the sensing performance for various applications are still the key to their commercialization. Here, a flexible strain sensor with a magnetic self-assembled pearl necklace-like microstructure is proposed, named PNS-C, which effectively improves its strain sensing performance by utilizing the curing magnetic induction intensity to adjust the arrangement of nanoparticles and multiwalled carbon nanotubes. It exhibited high sensitivity (GF = 361.02, 125–150% strain), nearly 40.16 times that of the corresponding randomly distributed structural composites, fast response (loading delay time was 132 ms), and high durability (1050 cycles). The sensing properties of PNS-C enable wearable health monitoring including facial micro-motion and knee movements. Finally, a manipulator grasping motion detection system is designed to demonstrate the potential practical value of the sensor in the field of robot control.

Noninvasive Human-Computer Interface Methods and Applications for Robotic Control: Past, Current, and Future.

The purpose of this study is to explore the noninvasive human-computer interaction methods that have been widely used in various fields, especially in the field of robot control. To have a deep understanding of the development of the methods, this paper employs “Mapping Knowledge Domains” (MKDs) to find research hotspots in the area to show the future potential development. Through the literature review, this paper found that there was a paradigm shift in the research of noninvasive BCI technologies for robotic control, which has occurred from early 2010 since the rapid development of machine learning, deep learning, and sensory technologies. This study further provides a trend analysis that the combination of data-driven methods with optimized algorithms and human-sensory-driven methods will be the key areas for the future noninvasive method development in robotic control. Based on the above findings, the paper provides a potential developing way of noninvasive HCI methods for related areas including health care, robotic system, and media.

Design of Robot Vision Servo Control System Based on Image

Visual servo is a closed-loop control system of robot, which takes the image information obtained by visual sensor as feedback. Generally speaking, visual servo plays an important role in robot control, which is one of the main research directions in the field of robot control and plays a decisive role in the development of intelligent robots. In order to make the robot competent for more complex tasks and work more intelligently, autonomously and reliably, it is necessary not only to improve the control system of the robot, but also to obtain more and better information about the working environment of the robot. This paper introduces the principle and basic realization method of robot visual servo based on image, and expounds the problems and solutions in image feature extraction and visual servo controller design. In order to further expand the application field of robots and improve the operation performance of robots, robots must have higher intelligence level and stronger adaptability to the environment, so as to manufacture intelligent robots that can replace human labor.

Motion Planning of Manipulators for Simultaneous Obstacle Avoidance and Target Tracking: An RNN Approach With Guaranteed Performance

Motion planning is a core issuein the field of robotic control, which directly affects the programming efficiency of robots. In this article, we study the motion planning problem of manipulators for simultaneous obstacle avoidance and target tracking, and propose a novel real-time planning method in a complex workspace. One important feature of the proposed method is that the robot can avoid colliding with obstacles by easily defining “virtual fences,” which are described by a group of level set functions. Thus, the feasible space can be abstracted as inequality constraints. Taking the predefined task, physical constraints, and feasible space constraints into consideration, the motion planning problem is formulated into a quadratic programming (QP) one, in which the redundant degrees of freedom are used to optimize the velocities of the robot. Then, the control command is obtained by an established recurrent neural network, which is capable of solving the QP problem in an online manner. Theoretical conduction and verification in several typical workspaces demonstrate the efficacy of the established method, such as the abilityto remove physical fences, quick rearrangement, and performance optimization.

An obstacles avoidance method for serial manipulator based on reinforcement learning and Artificial Potential Field

The obstacles avoidance of manipulator is a hot issue in the field of robot control. Artificial Potential Field Method (APFM) is a widely used obstacles avoidance path planning method, which has prominent advantages. However, APFM also has some shortcomings, which include the inefficiency of avoiding obstacles close to target or dynamic obstacles. In view of the shortcomings of APFM, Reinforcement Learning (RL) only needs an automatic learning model to continuously improve itself in the specified environment, which makes it capable of optimizing APFM theoretically. In this paper, we introduce an approach hybridizing RL and APFM to solve those problems. We define the concepts of Distance reinforcement factors (DRF) and Force reinforcement factors (FRF) to make RL and APFM integrated more effectively. We disassemble the reward function of RL into two parts through DRF and FRF, and make them activate in different situations to optimize APFM. Our method can obtain better obstacles avoidance performance through finding the optimal strategy by RL, and the effectiveness of the proposed algorithm is verified by multiple sets of simulation experiments, comparative experiments and physical experiments in different types of obstacles. Our approach is superior to traditional APFM and the other improved APFM method in avoiding collisions and approaching obstacles avoidance. At the same time, physical experiments verify the practicality of the proposed algorithm.

Tranceiver Multi Channels Untuk Kontrol Gerak dan Monitoring Kondisi Pada Robot Manual Mini Industri

Robotic technology that is developing rapidly triggers robotics developers to improve their abilities, especially in the field of robot control and monitoring. Today, robots are indeed present in human life in various forms. One type of robot that is often developed by robotics developers is the Industrial Mini Manual Robot. An industrial mini manual robot is a transporter robot that is equipped with a gripper and is controlled manually by the user through a joystick controller both wired and wireless. The manual robot with cable joystick control is very inefficient in its use so research on wireless joysticks is carried out. However, the current wireless joystick still uses the single-channel method, thereby eliminating multitasking capabilities such as those of the wired joystick robot. In this research, the design of multichannel transceivers for control and monitoring so that the robot has the ability to multitasking and the movement of the robot is not disturbed by wires. Multichannel transceivers use different frequency channels to transmit data. The command data and monitoring data can be transmitted simultaneously. This multi-channel transceiver has an average pairing time of 23.59 ms, a range of 116 meters in each channel and large power consumption of 59 mA.

HGG-CNN: The Generation of the Optimal Robotic Grasp Pose Based on Vision

Robotic grasping is an important issue in the field of robot control. In order to solve the problem of optimal grasping pose of the robotic arm, based on the Generative Grasping Convolutional Neural Network (GG-CNN), a new co... | Find, read and cite all the research you need on Tech Science Press

Collision-Free Compliance Control for Redundant Manipulators: An Optimization Case.

Force control of manipulators could enhance compliance and execution capabilities, and has become a key issue in the field of robotic control. However, it is challenging for redundant manipulators, especially when there exist risks of collisions. In this paper, we propose a collision-free compliance control strategy based on recurrent neural networks. Inspired by impedance control, the position-force control task is rebuilt as a reference command of task-space velocities, by combing kinematic properties, the compliance controller is then described as an equality constraint in joint velocity level. As to collision avoidance strategy, both robot and obstacles are approximately described as two sets of key points, and the distances between those points are used to scale the feasible workspace. In order to save unnecessary energy consumption while reducing impact of possible collisions, the secondary task is chosen to minimize joint velocities. Then a RNN with provable convergence is established to solve the constraint-optimization problem in realtime. Numerical results validate the effectiveness of the proposed controller.

Management of Power Supply System for Civil Aircraft

A control method based on Kinect for robots is proposed in this paper. For the motion control system of two-wheel robot, the method, which is combined with Kinect and DTW gesture recognition, is referred to the field of robotic control, and it can be applied in human-computer interaction system. A series of experiments indicate that this method can realize the dynamic gesture recognition and it has a good performance of real-time and robustness.

Dynamic neural networks based adaptive admittance control for redundant manipulators with model uncertainties

Force control is of great significance in the field of robotic control, for example, in tasks which requires precise control of contact force between robot and the environment, traditional path-tracking based methods may hardly achieve expected performance. However, it remains challenging for redundant manipulators with bounded constraints and model uncertainties. In this paper, we propose a novel adaptive admittance controller based on the idea of quadratic programming(QP). The control problem is transferred into an optimization one, in which the objective function is defined to make advantage of redundant DOFs, and the desired motion-force task is rebuilt as an equality constraints using admittance control strategy. Simultaneously, the bounded physical restrictions are regarded as inequality constraints. The uncertainties of both interaction model and physical parameters are also taken into consideration, which inspires us to use adaptive technology to identify those parameters online. Then an adaptive recurrent neural network is established to solve the QP problem online. This control scheme generalizes recurrent neural network based kinematic control of manipulators to that of position-force control, which opens a new avenue to shift position-force control of manipulators from pure control perspective to cross design with both convergence and optimality consideration. Besides, this method could deal with model uncertainties, and the calculation of pseudo-inverse of Jacobian matrix is no longer required. Numerical results on a 7-DOF manipulator iiwa and comparisons with existing methods show the validity of the proposed control method.

Kinematics analysis and self - collision detection of Truss type multi-robot cooperative welding platform

A collaborative welding platform consists of a XYZ-mobile truss, a 3R (revolute joint) clamping robot and a dual-arm welding robot. Based on Denavit-Hartenberg (D-H) method, the analytical inverse kinematics solution of the platform is obtained. Dual-arm coordination is a technical difficulty in robot control field. Focusing on self-collision problem during dual-arm robot motion planning, this paper proposes a new obstacle avoidance algorithm based on Rapidly Exploring Random Tree (RRT) algorithm. Setting searching area founded on artificial potential field (APF) algorithm and adopting note-pruning strategy achieves high searching efficiency. Simulation results verify the correctness of kinematics analysis and self-collision detection algorithm.

Transfer of Robot Perception Module With Adversarial Learning

It is a recent trend in the field of robotic control to collect large amount of data from simulated environments and then use it to train deep neural networks. However, there are many essential differences between simulated data and data from the real world. Thus, models trained naively on simulated data often fail to generalize to reality. To address that problem, we propose two approaches to transferring robot perception module, which are based on domain adversarial neural networks (DANN) and generative adversarial networks (GAN), respectively. The former approach tries to extract domain-invariant features by a shared feature extractor and use the domain-invariant features to train a transferrable target localization model (TLM). Meanwhile, the latter approach attempts to learn a transformation from the source domain to the target domain and use the transformation to generate realistic synthetic samples. Then, the synthetic samples are exploited as training data for the TLM. The experiments show that given enough simulated data and only a small amount of real world data, the TLM adapted by our methods could generalize well to real-world environments without drastic performance decline.

Research on Robot Control Technology Based on Kinect

With the development of robot control technology, the interaction between humans and robots has become more and more common. Using human movements and gestures to control the robot can replace complex and cumbersome program operations, easily and conveniently manipulate the robot, issue commands to the robot, and interact with the robot. Human vision and gesture recognition based on machine vision is an indispensable key technology for implementing a new generation of human-computer interaction systems. As a revolutionary product, Kinect somatosensory devices capture color images, depth images, and human bone images, and provide a new way of human-computer interaction. It captures and tracks human movements, gestures and sounds. In this paper, the Kinect somatosensory device is used for static gesture recognition and dynamic gesture recognition. Based on this, the robot action is used to realize the contactless interactive interaction of the robot through gesture control. This paper introduces a Kinect device-based robot control method, which is related to the field of robot control, and is applied to the two-wheeled self-balancing robot motion control and human-computer interaction system. This method utilizes Kinect for the motion control system of two-wheeled self-balancing robot. It is controlled with the DTW gesture recognition algorithm.

Path Planning for Robot based on Chaotic Artificial Potential Field Method

Robot path planning in unknown environments is one of the hot research topics in the field of robot control. Aiming at the shortcomings of traditional artificial potential field methods, we propose a new path planning for Robot based on chaotic artificial potential field method. The path planning adopts the potential function as the objective function and introduces the robot direction of movement as the control variables, which combines the improved artificial potential field method with chaotic optimization algorithm. Simulations have been carried out and the results demonstrate that the superior practicality and high efficiency of the proposed method.