Single Robot Research Articles

Novel Method for Improving Motion Accuracy of a Large-Scale Industrial Robot to Perform Offline Teaching Based on Gaussian Process Regression

Industrial robots that can respond to the current needs for variable-type and variable-volume production and that can play a variety of roles such as processing and transporting with a single robot to reduce time and cost. If realized, these robots will help save space in factories and increase production efficiency. However, this requires high positioning accuracy of the robot. In this study, we analyze the motion accuracy of industrial robots and their compensation method to construct this system. Here, we use a laser tracker to measure the coordinates of the hand tip of the robot when the robot is stationary. Subsequently, the error amount in an arbitrary posture is predicted using a Gaussian process. Furthermore, Bayesian optimization is used to efficiently search for points where the positioning error norm is likely to be large, which is then compensated for by a feedback method. This method successfully reduced the time cost of the experiment to approximately one-tenth of that required in the previous study and achieved a correction of approximately 66 %. However, because this method alone does not perform an exhaustive measurement, it is unclear whether all the points predicted to have small errors are so small that they do not require correction. Therefore, future studies, we will aim to verify this issue by considering the time efficiency.

The single robot line coverage problem: Theory, algorithms, and experiments

AbstractLine coverage is the task of servicing a given set of one‐dimensional features in an environment. It is important for the inspection of linear infrastructure such as road networks, power lines, and oil and gas pipelines. This paper addresses the single robot line coverage problem for aerial and ground robots by modeling it as an optimization problem on a graph. The problem belongs to the broad class of arc routing problems and is closely related to the rural postman problem (RPP) on asymmetric graphs. The paper presents an integer linear programming formulation with proofs of correctness. Using the minimum cost flow problem, we develop approximation algorithms with guarantees on the solution quality. These guarantees also improve the existing results for the asymmetric RPP. The main algorithm partitions the problem into three cases based on the structure of the required graph, that is, the graph induced by the features that require servicing. We evaluate our algorithms on road networks from the 50 most populous cities in the world, consisting of up to 730 road segments. The algorithms, augmented with improvement heuristics, run within 3 s and generate solutions that are within 10% of the optimum. We experimentally demonstrate our algorithms with commercial UAVs on the UNC Charlotte campus road network.

A single motor-driven continuum robot that can be designed to deform into a complex shape with curvature distribution

This paper proposes a method to deform a continuum robot into a complex shape with distributed curvature using a single motor drive. This continuum robot can be deformed to a desired shape by placing tendon guides at appropriate intervals. We used several target shapes, including clothoid and sin curves, as well as a circular curve of constant curvature and confirmed that the deformed shapes match them both in the simulation and prototype. This paper proposes two models of continuum robots. One is the Plain Model in which the tendons are parallel to the rod and the Penetration Model in which the tendon penetrates to the rod. By placing the penetrating position(s), this continuum robot can be deformed into a shape with inflection point(s). We designed a mathematical model to simulate the deformed shape of the prototype to obtain the proper placement of the guides and penetration point(s). Through the optimization, it was able to find the parameters that, in most cases, result in the error of less than 4% between the target and deformed shapes on simulation. We applied these conditions to the prototype and evaluated the errors, which were approximately 10%, the same as the related works that use a conventional constant curvature model. We think that the results of this paper can be applied to reduce the number of actuators required and the size and weight of continuum or biomimetic robots.

Optimized Computer Torque Control and Dynamic Model of a Spatial Single Section Continuum robot

In this paper, an Optimized Computer Torque Controller (OCTC) based on dynamic model for a variable continuum robot is elaborately developed. Basically, the OCTC is applied for the first time on continuum robots in this paper aiming at finding out the required robot’s cables tension as well as the robot’s orientation/bending angles during tracking any prescribed trajectory. Interestingly, particle swarm optimization (PSO) is used to optimize CTC’s parameters which enables the robot to perform tasks with high precision and stability, even in the presence of external perturbations or uncertainties. The proposed controller is evaluated by testing its ability to track multiple trajectories even in the presence of external perturbations. The results of these simulations show that the OCTC controller exhibits superior performance in terms of settling time, overshoot, and robustness against disturbances for the continuum robots. It is noteworthy to say that CTC is able to capture the behavior of the robot quite rapidly when the robot faces a disturbance which perfectly fits in with real-world applications of continuum robot.

Deep Reinforcement Learning for Real-Time Assembly Planning in Robot-Based Prefabricated Construction

The adoption of robotics is promising to improve the efficiency, quality, and safety of prefabricated construction. Besides technologies that improve the capability of a single robot, the automated assembly planning for robots at construction sites is vital for further improving the efficiency and promoting robots into practices. However, considering the highly dynamic and uncertain nature of a construction environment, and the varied scenarios in different construction sites, it is always challenging to make appropriate and up-to-date assembly plans. Therefore, this paper proposes a Deep Reinforcement Learning (DRL) based method for automated assembly planning in robot-based prefabricated construction. Specifically, a re-configurable simulator for assembly planning is developed based on a Building Information Model (BIM) and an open game engine, which could support the training and testing of various optimization methods. Furthermore, the assembly planning problem is modelled as a Markov Decision Process (MDP) and a set of DRL algorithms are developed and trained using the simulator. Finally, experimental case studies in four typical scenarios are conducted, and the performance of our proposed methods have been verified, which can also serve as benchmarks for future research works within the community of automated construction. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is conducted based on the comprehensive analysis of real-life assembly planning processes in prefabricated construction, and the methods proposed could bring many benefits to practitioners. Firstly, the proposed simulator could be easily re-configured to simulate diverse scenarios, which can be used to evaluate and verify the operations’ optimization methods and new construction technologies. Secondly, the proposed DRL-based optimization methods can be directly adopted in various robot-based construction scenarios, and can also be tailored to support the assembly planning in traditional human-based or human-robot construction environments. Thirdly, the proposed DRL methods and their performance in the four typical scenarios can serve as benchmarks for proposing new advanced construction technologies and optimization methods in assembly planning.

Robot swarms neutralize harmful Byzantine robots using a blockchain-based token economy.

Through cooperation, robot swarms can perform tasks or solve problems that a single robot from the swarm could not perform/solve by itself. However, it has been shown that a single Byzantine robot (such as a malfunctioning or malicious robot) can disrupt the coordination strategy of the entire swarm. Therefore, a versatile swarm robotics framework that addresses security issues in inter-robot communication and coordination is urgently needed. Here, we show that security issues can be addressed by setting up a token economy between the robots. To create and maintain the token economy, we used blockchain technology, originally developed for the digital currency Bitcoin. The robots were given crypto tokens that allowed them to participate in the swarm's security-critical activities. The token economy was regulated via a smart contract that decided how to distribute crypto tokens among the robots depending on their contributions. We designed the smart contract so that Byzantine robots soon ran out of crypto tokens and could therefore no longer influence the rest of the swarm. In experiments with up to 24 physical robots, we demonstrated that our smart contract approach worked: The robots could maintain blockchain networks, and a blockchain-based token economy could be used to neutralize the destructive actions of Byzantine robots in a collective-sensing scenario. In experiments with more than 100 simulated robots, we studied the scalability and long-term behavior of our approach. The obtained results demonstrate the feasibility and viability of blockchain-based swarm robotics.

Analysis of Kinematic Constraints in the Linkage Model of a Mecanum-Wheeled Robot and a Trailer with Conventional Wheels

Mechanical systems that consist of a four-wheeled or two-wheeled robot with Mecanum wheels and a two-wheeled trailer with conventional wheels are considered. The kinematic characteristics of the mechanical systems under consideration of holonomic and non-holonomic constraints are presented and compared. From this, it is shown that the structure of the kinematic constraint equations for mobile systems with a trailer does not apply to Chaplygin’s dynamic equations. If the mechanical system is not Chaplygin’s system, then the dynamic equations cannot be integrated separately from the equations of kinematic constraints. This is the difference between the kinematic constraint equations for the robot-trailer system and the constraint equations for a single robot with Mecanum wheels. Examples of numerical calculations using the equations of kinematic constraints are given.

Sliding mode control for Markovian jump systems subject to randomly occurring injection attacks: Handling aperiodic sampling issues

This paper investigates the sliding mode control for Markovian jump systems with the aperiodic sampling and cyber attacks, where the sampling period varies over a known interval and the system state is sampled and transmitted to the controller. Meanwhile, injection attacks may be randomly occurred when the control signals are transmitted to the actuator. The sampled-state based sliding mode controller is designed, where by choosing a mode-dependent sliding surface. With the aid of the improved looped functional, both the reachability of the specified sliding surface and the stochastic stability of the closed-loop system are analyzed. Furthermore, the genetic algorithm is used to search a desirable sliding matrix via minimizing the sliding domain. In the end, a single arm robot arm systems is applied to verify the proposed control scheme.

Self-Organized Patchy Target Searching and Collecting with Heterogeneous Swarm Robots Based on Density Interactions

The issue of searching and collecting targets with patchy distribution in an unknown environment is a challenging task for multiple or swarm robots because the targets are unevenly dispersed in space, which makes the traditional solutions based on the idea of path planning and full spatial coverage very inefficient and time consuming. In this paper, by employing a novel framework of spatial-density-field-based interactions, a collective searching and collecting algorithm for heterogeneous swarm robots is proposed to solve the challenging issue in a self-organized manner. In our robotic system, two types of swarm robots, i.e., the searching robots and the collecting robots, are included. To start with, the searching robots conduct an environment exploration by means of formation movement with Levy flights; when the targets are detected by the searching robots, they spontaneously form a ring-shaped envelope to estimate the spatial distribution of targets. Then, a single robot is selected from the group to enter the patch and locates at the patch’s center to act as a guiding beacon. Subsequently, the collecting robots are recruited by the guiding beacon to gather the patch targets; they first form a ring-shaped envelope around the target patch and then push the scattered targets inward by using a spiral shrinking strategy; in this way, all targets eventually are stacked near the center of the target patch. With the cooperation of the searching robots and the collecting robots, our heterogeneous robotic system can operate autonomously as a coordinated group to complete the task of collecting targets in an unknown environment. Numerical simulations and real swarm robot experiments (up to 20 robots are used) show that the proposed algorithm is feasible and effective, and it can be extended to search and collect different types of targets with patchy distribution.

Multi-robot cooperative autonomous exploration via task allocation in terrestrial environments.

Cooperative autonomous exploration is a challenging task for multi-robot systems, which can cover larger areas in a shorter time or path length. Using multiple mobile robots for cooperative exploration of unknown environments can be more efficient than a single robot, but there are also many difficulties in multi-robot cooperative autonomous exploration. The key to successful multi-robot cooperative autonomous exploration is effective coordination between the robots. This paper designs a multi-robot cooperative autonomous exploration strategy for exploration tasks. Additionally, considering the fact that mobile robots are inevitably subject to failure in harsh conditions, we propose a self-healing cooperative autonomous exploration method that can recover from robot failures.

Observer-based dynamic event-triggered secure control for nonlinear networked control systems with false data injection attacks

This paper considers the problem of observer-based dynamic event-triggered secure control for nonlinear networked control systems (NCSs) under false data injection attacks (FDIA). A novel output feedback controller is developed by utilizing a sampled-data observer to relieve the impacts of unmeasured states and FDIA. To make full use of the limited network resources, a dynamic periodic event-triggered mechanism (PETM) with an auxiliary variable is designed, which can reduce the transmission of information. Then, an approximation of injected false data is implemented based on an adaptive neural network (NN) in the presence of FDIA. Based on the proposed control strategy, the boundness of the nonlinear NCSs can be ensured by selecting some proper parameters. Finally, the effectiveness and validity of the proposed control strategy are illustrated via simulation of a single link robot arm system.

Fast terminal sliding mode control for dual arm manipulators

In this paper, present the recent advances in bimanual industrial manipulators have led to an increased interest in the specific problems pertaining to dual arm manipulation. This paper presents a control algorithm for dual arm robot that can move the object in a working plane both in translation and rotation ways. Different from other research that extend the control algorithms for a single robot to a dual arm robot because of fixed grasp assumption, this research has considered the frictional contact constraints to guarantee object grasping during moving of the object. Fast terminal sliding mode control (FTSMC) technique is used to design the controller and comparison to traditional and super-twisting sliding mode controls have been done. Simulations show the effectiveness and outperformance of the proposed control algorithm in comparison to considered sliding mode control techniques.

Open-architecture of CNC system and mirror milling technology for a 5-axis hybrid robot

This paper presents an open-architecture of CNC system and mirror milling technology for a new-type 5-axis hybrid robot named TriMule. The CNC system with dual CPUs is developed first to achieve human-computer interaction and motion control. Then, three key technologies are integrated in the system for improving the control quality, including singularity avoidance, feedforward control considering joint couplings and real-time error compensation by using externally mounted encoders. Based on these control technologies for single robot system, a collaborative machining strategy on the mirror milling system that consists of two TriMule robots is proposed to control the machining wall thickness of large thin-walled structural parts. Experiments on the TriMule robot and mirror milling system verify that the acceptable machining accuracy on the NAS test part and large thin-walled structural part can be ensured by using the developed CNC system and technologies. The root mean square of wall thickness error using the collaborative machining strategy can be 41.67% lower than the case without using the strategy.

Motion Analysis and Experiment of Multiple Magnetic Small-Scale Soft Robots

Since magnetic field is penetrating and harmless to human body, magnetic soft robots driven by magnetic field have great potential in medical fields. Thus, magnetic soft robots have attracted wide attention. However, the current researches mainly focus on the design of a single magnetic soft robot. Multiple magnetic soft robots also deserve to be studied due to their applications in collaborative operation. This paper presents a new design and fabrication method of multiple magnetic small-scale soft robots with different magnetic strength, size, and length-width ratio. The robots can be controlled to move in different motion modes and motion states under identical magnetic field. By analyzing their magnetic response property, which is the switching conditions between the two motion modes, and analyzing their states of the walking motion, two robots are selected from a batch of fabricated robots to carry out experiment. The results show that the two robots can move in different motion modes in the identical magnetic field.

Mutual visibility by fat robots with slim omnidirectional camera

In the existing literature of the Mutual Visibility problem for autonomous robot swarms, the adopted visibility models have some idealistic assumptions that are not consistent with practical sensing device implementations. This paper investigates the problem in the more realistic visibility model called opaque fat robots with slim omnidirectional camera. The robots are modeled as unit disks, each having an omnidirectional camera represented as a disk of smaller size. The region obstructed by a single robot in this model is a truncated infinite cone. This makes the visibility model significantly challenging compared to the previous ones. We assume that the robots have compasses that allow agreement in the direction and orientation of both axes of their local coordinate systems. The robots are equipped with visible lights which serve as a medium of communication and also as a form of memory. We present a distributed algorithm for the Mutual Visibility problem which is provably correct in the semi-synchronous setting. Our algorithm also provides a solution for Leader Election which we use as a subroutine in our main algorithm. Although Leader Election is trivial with two axis agreement in the full visibility model, it is challenging in our case and is of independent interest.

TMG: A topology-based motion generalization method with spatial relationship preservation

As an important requirement, human-friendly motion in human–robot interaction (HRI) is increasingly attracting attention. In many scenarios, the ability to generalize a similar motion from demonstration is essential for a robot, and the similarity is generally captured by the spatial relationship between the different joints of the robot. Though a lot of investigation for motion generalization has been conducted and good progress achieved, they share limitations in leaving the relationship out of consideration and being difficult to apply the generalization between different robots. In this paper, we propose a novel topology-based motion generalization (TMG) method that abstracts the motion generalization problem to a mesh deformation optimization, and the spatial relationship between different parts of the robot is captured with a topology-based representation. Instead of only taking into account a single joint position, the relationship semantic with Laplacian coordinates is modeled, and the motion generalization from demonstration to reproduction is realized by preserving the semantic as a Laplacian deformation, and even the robot or target position is changed. Furthermore, motion generalization between single or multiple different robots can be achieved with spatial relationship preservation and transfer. Our experimental results show that the reproduction based on topology-based representation outperforms the mapping methods by training with end-effector pose or joint angles, and ensures robust motion with spatial relationship preservation.

Robot Navigation in Complex Workspaces Employing Harmonic Maps and Adaptive Artificial Potential Fields.

In this work, we address the single robot navigation problem within a planar and arbitrarily connected workspace. In particular, we present an algorithm that transforms any static, compact, planar workspace of arbitrary connectedness and shape to a disk, where the navigation problem can be easily solved. Our solution benefits from the fact that it only requires a fine representation of the workspace boundary (i.e., a set of points), which is easily obtained in practice via SLAM. The proposed transformation, combined with a workspace decomposition strategy that reduces the computational complexity, has been exhaustively tested and has shown excellent performance in complex workspaces. A motion control scheme is also provided for the class of non-holonomic robots with unicycle kinematics, which are commonly used in most industrial applications. Moreover, the tuning of the underlying control parameters is rather straightforward as it affects only the shape of the resulted trajectories and not the critical specifications of collision avoidance and convergence to the goal position. Finally, we validate the efficacy of the proposed navigation strategy via extensive simulations and experimental studies.

Robot positioning and navigation technology is based on Integration of the Global Navigation Satellite System and real-time kinematics

This paper provides a new robot positioning and navigation technique that combines GNSS-RTK and visual SLAM to address the shortcomings of existing single robot positioning and navigation techniques, such as limited adaptation to scenarios and susceptibility to environmental influences. Real-time kinematics (RTK) is a real-time dynamic observation technique based on carrier phase observations, which can provide real-time 3D positioning results of the measurement site in a specified coordinate system, and can be positioned with centimetre-level accuracy, which is suitable for It is suitable for outdoor positioning of mobile robots. In this paper, a dataset is studied to compare and analyse the advantages and disadvantages of different SLAMs in order to determine the most suitable robot positioning and navigation technique for autonomous indoor and outdoor mobility. These sensors were well calibrated and the composition was applied to the RK3588. The results show good performance in indoor-outdoor switching scenarios, obtaining a mapping technique that cannot be achieved with a single visual SLAM, effectively improving the ability to adapt to multiple scenarios and enhancing the operational range of the transportable robot.

WebRTC-based MOSR remote control of mobile manipulators.

This article describes a contribution to the field of telerobotics via the Internet through the development of a web-based platform allowing the remote control of robots by multiple users, simultaneously. It also deals with minimizing the execution times of tasks by reducing connection and interaction delays. For this purpose, the Web Real-Time Communication (WebRTC) technology is utilized. The developed remote manipulation system allows the operators to visualize the robot, its surroundings and the data incoming from its sensors, and to carry out basic tasks, either independently by the manipulator or by the mobile robot, or jointly by both mechanical sub-systems. In addition, to ensure the control of the remote robot by several operators simultaneously, a priority system managing parallel tasks and a chat system between the operators have been proposed. Besides, many teams are able to exploit the robot, concurrently. The WebRTC-based Multiple Operator Single Robot (MOSR) telerobotic platform is validated on the emulator of the RobuTER/ULM mobile manipulator through various scenarios of primitive tasks over the Internet.

Multimodal-Driven Magnetic Microrobots with Enhanced Bactericidal Activity for Biofilm Eradication and Removal from Titanium Mesh.

Modern micro/nanorobots can perform multiple tasks for biomedical and environmental applications. Particularly, magnetic microrobots can be completely controlled by a rotating magnetic field and their motion powered and controlled without the use of toxic fuels, which makes them most promising for biomedical application. Moreover, they are able to form swarms, allowing them to perform specific tasks at a larger scale than a single microrobot. In this work, they developed magnetic microrobots composed of halloysite nanotubes as backbone and iron oxide (Fe3 O4 ) nanoparticles as magnetic material allowing magnetic propulsion and covered these with polyethylenimine to load ampicillin and prevent the microrobots from disassembling. These microrobots exhibit multimodal motion as single robots as well as in swarms. In addition, they can transform from tumbling to spinning motion and vice-versa, and when in swarm mode they can change their motion from vortex to ribbon and back again. Finally, the vortex motion mode is used to penetrate and disrupt the extracellular matrix of Staphylococcus aureus biofilm colonized on titanium mesh used for bone restoration, which improves the effect of the antibiotic's activity. Such magnetic microrobots for biofilm removal from medical implants could reduce implant rejection and improve patients' well-being.