Robot Working Research Articles

МОДЕЛИРОВАНИЕ КИНЕМАТИЧЕСКИХ И ДИНАМИЧЕСКИХ ХАРАКТЕРИСТИК РОБОТА-МАНИПУЛЯТОРА С ИСПОЛЬЗОВАНИЕМ ВИРТУАЛЬНОГО ПРОТОТИПА

The article presents a method for modeling the kinematic and dynamic parameters of the robot manipulator model KUKA KR 6 R900-2. The simulation is performed using a virtual prototype of a robot designed in the KOMPAS 3-D computer-aided design system and exported to the MSC Adams software package. The prototype of the robot is represented by idealized parts corresponding to the real links of the structure. The joints of the links are implemented by articulated joints. Various options of the robot working out a given trajectory of movement with varying the angular velocity of its turns and the applied load of the potential load are investigated: the maximum speed at the maximum weight of the load, the nominal speed at the nominal weight of the load and the minimum speed at the minimum weight of the load. The specified trajectory of movement is divided into separate stages, a certain set of workloads is applied during the simulation in each. The simulation is carried out in a three-coordinate space. The result of the simulation is kinematic parameters, such as movement, speed, acceleration, and dynamic parameters – moments in the robot's joints. The obtained results allow to perform a comparative analysis of the operating loads at different stages of the trajectory, to identify unfavorable conditions, such as critical peak values of accelerations and emerging moments of forces

Row End Detection and Headland Turning Control for an Autonomous Banana-Picking Robot

A row-following system based on machine vision for a picking robot was designed in our previous study. However, the visual perception could not provide reliable information during headland turning according to the test results. A complete navigation system for a picking robot working in an orchard needs to support accurate row following and headland turning. To fill this gap, a headland turning method for an autonomous picking robot was developed in this paper. Three steps were executed during headland turning. First, row end was detected based on machine vision. Second, the deviation was further reduced before turning using the designed fast posture adjustment algorithm based on satellite information. Third, a curve path tracking controller was developed for turning control. During the MATLAB simulation and experimental test, different controllers were developed and compared with the designed method. The results show that the designed turning method enabled the robot to converge to the path more quickly and remain on the path with lower radial errors, which eventually led to reductions in time, space, and deviation during headland turning.

Prevention of Covid-19 affected patient using multi robot cooperation and Q-learning approach: a solution.

Combat with the novel corona virus (COVID-19) has become challenging for all the frontline warriors like, medic people, police and other service provider. Many technology and intelligent algorithms have been developed to set the boundary in its incremental growth. This paper proposed a concept to set the boundary on spreading of this disease among the medic people, who are directly exposed to the COVID-19 patient. To reduce their risk to be infected, we have designed the theoretical model of the medic robot to provide medical services to the confirmed case patient. This paper explains the deployment and execution of assigned work of medic robot for patient carrying, delivering food, medications and handling the emergency health services. The medic robots are divided into various group based on their works. The COVID-19 area is considered as a multi-robot environment, where multiple medic robots will work simultaneously. To achieve the multi-robot cooperation and collision avoidance we have implemented the simplest reinforcement learning approach i.e. the Q-learning approach. We have compared the result with respect to the improved-Q-learning approach. A comparative analysis based on parameters like simplicity, objective, deployed robot category and cooperation has been done with some other approaches mentioned in the literature. For simplicity as well as the time and space complexity purpose the results reveal that Q-learning approach is a better consideration. The proposed approach reduces the mortality rate by 2%.

Electromanyetik Silahlı Terörle Mücadele Robotu

This article proposes the integration of electromagnetic weapons on a robot, design and construction of an electromagnetic armed robot, autonomous targeting of possible targets with the electromagnetic weapon, and the features a electromagnetic armed safety robot should have. Unlike traditional user-targeted field security robot approaches, the robot mentioned in this study detects potential threats in the task area with image processing and artificial intelligence techniques, so the user can accurately identify and autonomously target targets without the need for controlled targeting. Unlike today’s armed robots, an electromagnetic armed robot, which will be a new literature study, has been developed to create a reference path. An electromagnetic weapon that can be carried by a robot is produced and integrated into the robot and a new armed robot approach with electromagnetic weapon is introduced. Various methods are proposed considering the range restriction of electromagnetic weapons and possible targeting errors of the robot user. A control algorithm has been developed to have the most appropriate targeting under the dynamic constraints of the robot and user for target tracking. Prototyping and experiments show the ability of an autonomous security robot with an autonomous targeting system to troubleshoot user problems and targeting problems. In addition, various methods and recommendations are provided for the features that a electromagnetic armed security robot working in the field should have.

Reading the Human through Robots

Reading the Human through Robots

Enabling Flexibility in Manufacturing by Integrating Shopfloor and Process Perception for Mobile Robot Workers

Robotic flexibility in industry is becoming more and more relevant nowadays, especially with the rise of the Industry 4.0 concept. This paper presents a smart execution control framework for enabling the autonomous operation of flexible mobile robot workers. These robot resources are able to autonomously navigate the shopfloor, undertaking multiple operations while acting as assistants to human operators. To enable this autonomous behavior, the proposed framework integrates robot perception functions for the real-time shopfloor and process understanding while orchestrating the process execution. A Digital World Model is deployed synthesizing the sensor data coming from multiple 2D and 3D sensors from the shopfloor. This model is consumed for the perception functions enabling the real-time shopfloor and process perception by the robot workers. This smart control system has been applied and validated in a case study from the automotive sector.

An integrated rough ELECTRE II approach for risk evaluation and effects analysis in automatic manufacturing process

Smart manufacturing is an essential part of fourth industrial revolution in which robotic machines can control and perceive automatically to provide effectiveness and convenience in production process. However, the existence of potential failures and defects not only influence the manufacturing process but also damages the resources and cause negative impacts on environment. Failure modes and effects analysis (FMEA) is a key approach to identify and eliminate possible failures and evaluate the risks from design, system and process. This research paper provides a novel FMEA approach for risk evaluation by integrating rough set theory and ELimination and Choice Translating REality (ELECTRE) II method to handle the subjectivity and uncertainty in experts’ judgements without much prior information, membership functions and additional adjustments. Rough numbers are used to study uncertainty in linguistic terms using intervals instead of single fixed values. The proposed approach is formulated by defining different types of concordance and discordance sets using optimization techniques based on statistical dispersion and maximum deviation method. The presented technique shows the strong, weak and neutral pairwise relations among failure modes by systemically comparing them from each risk component. The distance functions and averaging methods are applied to check the similarities and differences among error modes which improves the accuracy of the results. The developed rough FMEA approach is applied to identify the potential failures of robot working in optical cable industry and evaluate the risk components of manufacturing and production process. Rough ELECTRE II approach can be effectively applied to enhance the efficiency of working conditions and prevent the loss of crude materials and energy.

The relevance of causation in robotics: A review, categorization, and analysis

Abstract In this article, we investigate the role of causal reasoning in robotics research. Inspired by a categorization of human causal cognition, we propose a categorization of robot causal cognition. For each category, we identify related earlier work in robotics and also connect to research in other sciences. While the proposed categories mainly cover the sense–plan–act level of robotics, we also identify a number of higher-level aspects and areas of robotics research where causation plays an important role, for example, understandability, machine ethics, and robotics research methodology. Overall, we conclude that causation underlies several problem formulations in robotics, but it is still surprisingly absent in published research, in particular when it comes to explicit mentioning and using of causal concepts and terms. We discuss the reasons for, and consequences of, this and hope that this article clarifies the broad and deep connections between causal reasoning and robotics and also by pointing at the close connections to other research areas. At best, this will also contribute to a “causal revolution” in robotics.

On the Performance Analyses of a Modified Force Field Algorithm and Neural Network Approach for Obstacle Avoidance in Swarm Robotics

Obstacle avoidance is a major hurdle when implementing mobile robots and swarm robots. Swarm robots work in groups and therefore require an efficient and functional obstacle avoidance algorithm to stay collision free between themselves and their surroundings. This paper reviews previous research in obstacle avoidance implementation using the force field method (FFM), also known as potential field method (PFM) and a neutral network approach. Moreover, this paper aims to execute simulations using a modified force field algorithm and a neural network approach and compare them. The obtained results are analyzed to identify the performance characteristics and the time taken to perform tasks using a singular mobile robot against a swarm robot environment consisting of four and ten robots, respectively, in both simulation cases. Simulations showed that the algorithm was successful in navigating obstacles for both single and swarm robot environments. A single robot was found to take up to 340% longer to arrive at the required location compared to the first robot in the experiment. Moreover, it was found that the neural network approach showed ~ 27% improvement over the modified force field algorithm when it comes to cases where more than four robots are being used.

Robots Asking for Favors: The Effects of Directness and Familiarity on Persuasive HRI

As robots work in increasingly collaborative settings with humans, scenarios will arise where robots need to successfully request favors to be effective. Communication directness and familiarity have been shown to be important factors to persuasion. However, these two critical factors have not yet been jointly investigated in human-robot interaction (HRI). This letter explores how they can be used by social robots to request favors from people. We present a social HRI study that uniquely investigates the effects of a robot's communication directness and its familiarity on persuasiveness, trustworthiness, and a person's willingness to help the robot. In the study, we present participants with scenarios where two different robots (one familiar, one unfamiliar) ask participants for favors using either direct or indirect requests. Our results show that a familiar robot is more persuasive, trustworthy, and people are more willing to help it. Furthermore, indirect requests are perceived to be more trustworthy and encourage greater willingness to help than direct requests, regardless of the robot's familiarity. Further discussions of these results highlight key considerations for collaborative social robots, particularly when robots request assistance or favors from people.

Digital Plan of Brickwork Layout for Robotic Bricklaying Technology

The trend of using modern technologies in the construction industry has been growing stronger recently, particularly in the fields of additive construction or robotic bricklaying. Therefore, specifically for the purpose of robotic bricklaying, we created a digital layout plan for robotic construction works. This article presents a universal methodology for creating a bricklaying plan for various variations of wall building systems. The method is based on the conversion of drawings from the BIM (Building Information Model) environment to the BREP (Boundary Representation) model through use of the IFC (Industry Foundation Classes) format, which simultaneously divides object models into layers and connects discontinuous wall axes by means of an orthogonal arrangement and inserting details into critical structural points. Among other aspects, the developed algorithm proposes the optimal placement of the robotic system inside objects under construction, in order to minimize the distance of the robot’s movement and to reduce its electricity consumption. Digital layout plans created in this way are expected to serve as a stepping stone for robotic bricklaying.

A Safety-Aware Kinodynamic Architecture for Human-Robot Collaboration

The new paradigm of human-robot collaboration has led to the creation of shared work environments in which humans and robots work in close contact with each other. Consequently, the safety regulations have been updated addressing these new scenarios. The mere application of these regulations may lead to a very inefficient behavior of the robot. In order to preserve safety for the human operators and allow the robot to reach a desired configuration in a safe and efficient way, a two layers architecture for trajectory planning and scaling is proposed. The first layer calculates the nominal trajectory and continuously adapts it based on the human behavior. The second layer, which explicitly considers the safety regulations, scales the robot velocity and requests for a new trajectory if the robot speed drops. The proposed architecture is experimentally validated on a Pilz PRBT manipulator.

Swarm-based painting of an area cluttered with obstacles

ABSTRACT This article studies the problem of painting of a rectangular region cluttered with horizontal obstacles, by a swarm of mobile robots. Initially, the robots are deployed randomly within the target area. From the initial configuration, the robots are assembled on the left boundary of the region. Upon assembling on the left boundary, the robots first explore the entire area collectively to make a virtual partition of it and then collectively paint the target region in a subsequent phase. The proposed algorithm assumes that robots work in look–compute–move model. The robots follow a completely distributed algorithm to paint the region. The robots are either synchronous or semi-synchronous. The outcome of the proposed algorithm is a complete painting of an area without any repetition and collision.

Design and Fabrication of Robotic Arm for the assembly of Phase Selector Switch

This paper explores the robot arm where the ability of human to do a project is restrained however now not with the aid of his mental electricity however through his bodily energy. A humanoid mechanical technology is another difficult field. To co-work with people, humanoid robots not just need to include human like structure and structure, yet more significantly, they should arrange human like conduct with respect to the movement, correspondence and intelligence. In environmental factors where human exchange can't be conceivable to do a specific errand, the robots can do. Robots are used for lean industrial processes and have diversified their contributions to meeting lines inside the production international. The main focus of this project is to design and develop the mechanism for robotic arm for lifting. It is a type of mechanical arm, usually programmable, with similar functions to a human arm. The arm might be a unit system or might be an aspect of a more unpredictable mechanical cycle. The end effectors or mechanical hand can be intended to play out any ideal undertaking, for example, welding, holding, turning etc., depending on the application. The mechanical arm is planned and designed with four degrees of freedom and modified to achieve precisely basic light material lifting undertaking to aid the creation line in any industry. 3D printing strategy is utilized in this undertaking to create the parts of the automated arm. Hence, it gave more exact measurements and gigantic time and cost expensive in creation. The automated arm is furnished with 4 servo engines to interface the parts and bring arm development. A sequential construction mechanical system can improve its efficiency through developing the assembling pace and its consistency. They additionally spare individuals from dreary and tedious mechanical production system employments. Many industries are yet to implement such automation in their meeting traces, as they do no longer have the technical recognize how of the changeover, or the concern of failure of investment. It is proposed to layout and increase a low price assembly robotic which could overcome the above drawbacks. We have also included Design Thinking process before starting to do this experiment. The stages of Design Thinking such as Empathize, Define, Ideate, Prototype & test, Evolve are performed in the complete fabrication of the robotic arm. Many industries are yet to implement such automation in their assembly lines, as they do not have the technical knowhow of the changeover, or the fear of failure of investment. To proceed in this direction, a case study was conducted at a phase selector switch making company. Presently the company does manual assembly of the switches, which is a hindrance to the growth of the company. This process required a pilot study to explore the possibility as well as the implementations issues of robots in the assembly. The present system uses a belt conveyor system which has no feedback signals and the speed of the conveyor is not synchronized with the rate of assembly. It is generally not advised to transfer small size components in a conveyor since picking, orienting and inserting the components will be cumbersome. When the size of the component is considerably small, manual assembly is not recommended as per the guidelines of DFMA (Design for manufacture and assembly). Manual error is more prone to assembly of the part in a wrong orientation, especially for those components that are symmetrical.The parts to be assembled are both axi-symmetric as well as prismatic in shape. Constraints in loading and unloading of casing were also reported. In this work, the design and development of a low cost assembly robot is done which can overcome the above drawbacks.

Robots and COVID-19: Re-imagining Human–Robot Collaborative Work in Terms of Reducing Risks to Essential Workers

The COVID-19 pandemic has led to the widespread adoption of physical distancing to prevent the disease’s spread. Physical distancing, however, is not always feasible for essential workers. Robots are one proposed solution to help ensure that essential work is performed while reducing the risk of COVID-19 exposure among essential workers and their families. The COVID-19 pandemic has, however, highlighted the ability and inability of robots to directly replace human labor. At present, much of the discussion has focused on the need for technical developments in robotics. This perspective is short-sighted because it and fails to leverage the collaborative nature of work between humans and robots. In response, this article acts as a call to shift the conversation away from technical developments and toward a focus on human and robot work redesign.

Feasibility, planning and control of ground-wall transition for a suctorial hexapod robot

One of the key factors that affect the efficiency and scope of work of wall-climbing robots is how the climbing robot can achieve autonomous transition between adjacent vertical planes. This paper studies the problem of ground-wall transition of a self-developed suctorial wall-climbing hexapod robot (WelCH). In view of the feasibility of the robot performing transition, this paper makes a detailed analysis of the number and property of degrees of freedom (DOFs) of the body and the foot based on reciprocal screw theory, and the results show that the robot can achieve transitional motion only when its home configuration is axisymmetric rather than radially symmetric. For realizing the robot’s ground-wall transition, based on a Sinusoid-Sigmoid-Shaped (SS-Shaped) interpolation function, the motion strategies of foot transferring and body pitching are firstly designed in detail. This interpolation method can effectively avoid the wear of the suction cups by relying on fewer essential path points. Then, the saturation-truncated method and mean filtering method are used to deal with joint constraints and abrupt changes in angular velocities. Finally, a kinematic-based adaptive sliding mode control (ASMC) is adapted to track the planned smooth trajectory, which can effectively resist bounded external disturbances. The successful transitions from the horizontal ground to the vertical wall for the robot WelCH in simulation and filed experiment demonstrate the effectiveness of the proposed strategy.

Toward Teaching by Demonstration for Robot-Assisted Minimally Invasive Surgery

Learning manipulation skills from open surgery provides more flexible access to the organ targets in the abdomen cavity and this could make the surgical robot working in a highly intelligent and friendly manner. Teaching by demonstration (TbD) is capable of transferring the manipulation skills from human to humanoid robots by employing active learning of multiple demonstrated tasks. This work aims to transfer motion skills from multiple human demonstrations in open surgery to robot manipulators in robot-assisted minimally invasive surgery (RA-MIS) by using TbD. However, the kinematic constraint should be respected during the performing of the learned skills by using a robot for minimally invasive surgery. In this article, we propose a novel methodology by integrating the cognitive learning techniques and the developed control techniques, allowing the robot to be highly intelligent to learn senior surgeons' skills and to perform the learned surgical operations in semiautonomous surgery in the future. Finally, experiments are performed to verify the efficiency of the proposed strategy, and the results demonstrate the ability of the system to transfer human manipulation skills to a robot in RA-MIS and also shows that the remote center of motion (RCM) constraint can be guaranteed simultaneously. Note to Practitioners-This article is inspired by limited access to the manipulation of laparoscopic surgery under a kinematic constraint at the point of incision. Current commercial surgical robots are mostly operated by teleoperation, which is representing less autonomy on surgery. Assisting and enhancing the surgeon's performance by increasing the autonomy of surgical robots has fundamental importance. The technique of teaching by demonstration (TbD) is capable of transferring the manipulation skills from human to humanoid robots by employing active learning of multiple demonstrated tasks. With the improved ability to interact with humans, such as flexibility and compliance, the new generation of serial robots becomes more and more popular in nonclinical research. Thus, advanced control strategies are required by integrating cognitive functions and learning techniques into the processes of surgical operation between robots, surgeon, and minimally invasive surgery (MIS). In this article, we propose a novel methodology to model the manipulation skill from multiple demonstrations and execute the learned operations in robot-assisted minimally invasive surgery (RA-MIS) by using a decoupled controller to respect the remote center of motion (RCM) constraint exploiting the redundancy of the robot. The developed control scheme has the following functionalities: 1) it enables the 3-D manipulation skill modeling after multiple demonstrations of the surgical tasks in open surgery by integrating dynamic time warping (DTW) and Gaussian mixture model (GMM)-based dynamic movement primitive (DMP) and 2) it maintains the RCM constraint in a smaller safe area while performing the learned operation in RA-MIS. The developed control strategy can also be potentially used in other industrial applications with a similar scenario.

Design and analysis of gantry robot for pick and place mechanism with Arduino Mega 2560 microcontroller and processed using pythons

Abstract Robots are extensively used in industries for their precision work and amount of work that one can obtain without any defects. In this paper we are using a gantry robot for as it does not occupy the floor space therefore reducing the distance for reachability of the parts and hence reducing unnecessary material for guide way. Robots work in strictly defined path and there is no or very little change in such systems in order to overcome this we are using a vision based control system to make the system dynamic in nature the images are picked by using a USB camera processed images of the object is transmitted via serial communication to the Arduino Mega 2560 microcontroller and processed using pythons open source computer vision (Open CV) image to process the image captured by the USB camera to find the exact colour and to pick the object and sort it.

Automated anomalies detection in the work of industrial robots

This article describes the results of the anomalies automated detection algorithm development in the operation of industrial robots. The development of robotic systems, in particular, industrial robots, and software for them is ahead of the tracking and managing technologies development. The operation of the digital production system involves the generation of a large amount of various data characterizing the state of both the specific equipment and the industrial system as a whole. Such a system produces a sufficient amount of data to develop machine learning models to analyse this data to solve problems such as forecasting and modelling. As part of the study, an experiment was conducted based on the equipment of the laboratory of industrial robots of Tomsk Polytechnic University. In the course of the research, the industrial manipulator moved loads belonging to different classes by weight. An algorithm was developed for the automated analysis of the values of the parameters of the consumed current and the position of the manipulator.

Vision-Based Associative Robotic Recognition of Working Status in Autonomous Manufacturing Environment

Recognition of working status and resolving abnormal conditions during the manufacturing process commonly relies on human intervention to visually inspect and adjust, which is boring, repetitive, and sometimes risky. In order to achieve completely autonomous manufacturing, a vision-based robotic associative working status recognition method is proposed. This study aims to recognize the working status of HAAS CNC machine in autonomous manufacturing environment using ‘scene text recognition’, in an effort to develop autonomous machine tending solution. The result of this study based on vision input processing and Convolutional Recurrent Neural Networks (CRNN) has a recognition accuracy of 97.3%, which is a good performance.