Virtual tools that carry attributes for interactively specifying intermediate manufacturing processes

This paper describes an interactive system for specifying robotic tasks using virtual tools that allow an operator to reach into a live video scene and direct robots to use corresponding real tools in complex scenarios that involve integrating a variety of otherwise autonomous technologies. The attribute rich virtual tools concept provides a human-machine interface that is robust to unanticipated developments and tunable to the specific requirements of a particular task. This Interactive Specification concept is applied to intermediate manufacturing tasks.     

Fuzzy multi-criteria evaluation of industrial robotic systems

Industrial robots have been increasingly used by many manufacturing firms in different industries. While the number of robot manufacturers is also increasing with many alternative ranges of robots, potential end-users are faced with many options in both technical and economical factors in the evaluation of the industrial robotic systems. Industrial robotic system selection is a complex problem which many qualitative attributes must be considered. These kinds of attributes make the evaluation process hard and vague. Hierarchical structure is a good approach to describe a complicated system. This paper proposes a fuzzy hierarchical TOPSIS model for the multi-criteria evaluation of the industrial robotic systems. An application is presented with some sensitivity analyses by changing the critical parameters.     

Design of a modular assembly of four-footed robots with multiple functions

Modern industries use many types of robots. In addition to general robotic arms, bipedal, tripedal, and quadrupedal robots, which were originally developed as toys, are gradually being used for multiple applications in manufacturing processes. This research begins with establishing the platform for four-footed robots with multiple functions, high sensitivity, and modular assembly and this is how a fundamental model of the industrial robots is constructed. Under additional loads, the four feet of the quadrupedal robot reinforce its carrying ability and reliability compared to bipedal or tripedal robots, which helps it to carry more objects and enhances functionality. Based on different requirements and demands from the manufacturing processes, the highly sensitive four-footed robot provides an expandable interface to add different sensing components. In addition, when combined with a wireless communication module or independent 1.2 GHz radio frequency CCD wireless image transmission system, the user can control the robot remotely and instantly. The design helps the four-footed robot to expand its applications. By assembling and disassembling modules and changing the sensing components, the highly sensitive four-footed robot can be used for different tasks. Moreover, the remote control function of the robot will increase interaction with human beings, so it can become highly become involved in people's lives. The platform of the four-footed robot will become a design reference for the commercialization of different industrial robots, and it will provide the design of industrial robots with more options and useful applications.     

A hybrid metaheuristic algorithm to optimise a real-world robotic cell

In this paper, a real-world robotic cell is investigated by transforming it into a special job shop with a set of stationary robots for manufacturing the parts of a product (i.e., operations of a job) at multiple operational stages. In addition, this robotic cell contains a particular mobile robot to transport the parts among stationary robots inside the cell as well as a depot (for initialising the production) and a stockpile (for stocking the complete products) outside the cell. Thus, a new scheduling problem called Blocking Job Shop Scheduling problem with Robotic Transportation (BJSSRT) is proposed. A numerical example is presented to illustrate the characteristics and complexity of BJSSRT. According to the problem properties, four types of robotic movements are defined for a mobile robot in an operation’s execution: processing-purpose, depot-purpose, return-purpose and stocking-purpose. By satisfying complex feasibility conditions, an innovative graph-based constructive algorithm is developed to produce a good feasible BJSSRT schedule. Embedded with the constructive algorithm, a hybrid Tabu Search and Threshold Accepting metaheuristic algorithm is developed to find a near-optimal solution in an efficient way. The proposed BJSSRT methodology has practical benefits in modelling the automated production system using stationary and mobile robots, especially in manufacturing and mining industries.     

Symbiotic robotic systems: humans, robots, and smart environments

In general symbiotic robotic systems can be considered three-agent systems in which the agents are performing interdependent tasks. In such systems the robot's actions are generally controllable and observable the environment's actions are partially controllable and partially observable, and the human's actions are not (directly) controllable but are partially observable. This new paradigm will enable us to incorporate techniques from AI and robotics in everyday, smart environments. Symbiotic robotic systems will shift our focus from the development of fully autonomous, almighty robot servants to the development of an open, extensible environment in which humans will cohabitate with many cooperating robotic devices.     

A Study on Development of a Hybrid Aerial/Terrestrial Robot System for Avoiding Ground Obstacles by Flight

To date, many studies related to robots have been performed around the world. Many of these studies have assumed operation at locations where entry is difficult, such as disaster sites, and have focused on various terrestrial robots, such as snake-like, humanoid, spider-type, and wheeled units. Another area of active research in recent years has been aerial robots with small helicopters for operation indoors and outdoors. However, less research has been performed on robots that operate both on the ground and in the air. Accordingly, in this paper, we propose a hybrid aerial/terrestrial robot system. The proposed robot system was developed by equipping a quadcopter with a mechanism for ground movement. It does not use power dedicated to ground movement, and instead uses the flight mechanism of the quadcopter to achieve ground movement as well. Furthermore, we addressed the issue of obstacle avoidance as part of studies on autonomous control. Thus, we found that autonomous control of ground movement and flight was possible for the hybrid aerial/terrestrial robot system, as was autonomous obstacle avoidance by flight when an obstacle appeared during ground movement.      

A fail-safe tele-autonomous robotic system for nuclear facilities

A fail-safe tele-autonomous robotic system is proposed for use in advanced nuclear reprocessing facilities. The design exploits the technologies developed for space telerobotics. The target system consists of a graphical user interface for an operator to execute robotic tasks, hand controllers for teleoperation, a three-dimensional graphical simulator, and robot control software to drive both the graphical simulation and dual six degree-of-freedom robots to perform tasks using autonomous, teleoperated, and shared control modes. A preliminary design for a safety monitoring system for fail-safe operations is also described.     

Application of robotics in onshore oil and gas industry—A review Part I

With ever increasing global demand and depleting resources for fossil fuels, oil and gas industry is now positively looking for advanced robotic solutions to increase their productivity and safety. With time easy resources of the fossil fuels are shrinking and newly searched reservoirs, to feed supply demands of global consumption, are mostly located in extreme environmental conditions such as hot deserts, deep water and arctic zone etc. Production of the fossil fuels, in such inhospitable environmental conditions, poses difficult challenges to health, safety and environment (HSE). Tragic incidents like Exxon Valdez and Deepwater Horizon oil spills are examples of such challenges. Therefore, oil and gas industry has lot to learn from successful implementation of robotics and automation for dull, dirty and dangerous (3D) tasks of manufacturing industry. Most of the robotics technologies, currently used in the oil and gas industry, are mainly focused on inspection, maintenance and repair (IMR) of plant facilities with higher frequency and accuracy. Fundamental idea, involved in the automatization of these processes, is based on the principle of teleoperation with skilled operator. Automation of 3D tasks not only improves HSE standards but also lead to much needed economic efficiency by reducing production cycle, floor space and number of staff members required for continuous inspection and manipulation of plant facilities. Considering the risks involved in this industry usage of completely autonomous robots, first without achieving very high reliability, is still a far fetch choice. Therefore, semi-autonomous robots, where actions are performed by robots but cognitive decisions are still taken by skilled operator, is an excellent choice for this industry as a near future solution. In the onshore oil and gas industry robotic solutions are used both in upstream and downstream processes, such as site survey, drilling, production and transportation, mainly focused in the form of in-pipe inspection robots (IPIRs), tank inspection robots (TIRs), unmanned aerial vehicles (UAVs) and wireless sensor networks (WSNs) etc. This paper presents the state of art robotic solutions currently used in onshore oil and gas facilities.     

Introduction to the special issue on Advances in intelligent nonlinear control for robotic systems

In the last two decades, robotic systems have achieved wide applications in every aspect of human society, including industrial manufacturing, automotive production, medical devices, and social lives. With the     

Reinforcement learning for facilitating human-robot-interaction in manufacturing

For many contemporary manufacturing processes, autonomous robotic operators have become ubiquitous. Despite this, the number of human operators within these processes remains high, and as a consequence, the number of interactions between humans and robots has increased in this context. This is a problem, as human beings introduce a source of disturbance and unpredictability into these processes in the form of performance variation. Despite the natural human aptitude for flexibility, their presence remains a source of disturbance within the system and make modelling and optimization of these systems considerably more challenging, and in many cases impossible. Improving the ability of robotic operators to adapt their behaviour to variations in human task performance is, therefore, a significant challenge to be overcome to enable many ideas in the larger intelligent manufacturing paradigm to be realised. This work presents the development of a methodology to effectively model these systems and a reinforcement learning agent capable of autonomous decision-making. This decision-making provides the robotic operators with greater adaptability, by enabling its behaviour to change based on observed information, both of its environment and human colleagues. The work extends theoretical knowledge on how learning methods can be implemented for robotic control, and how the capabilities that they enable may be leveraged to improve the interaction between robots and their human counterparts. The work further presents a novel methodology for the implementation of a reinforcement learning-based intelligent agent which enables a change in behavioural policy in robotic operators in response to performance variation in their human colleagues. The development and evaluation are supported by a generalized simulation model, which is parameterized to enable appropriate variation in human performance. The evaluation demonstrates that the reinforcement agent can effectively learn to make adjustments to its behaviour based on the knowledge extracted from observed information, and balance the task demands to optimise these adjustments.     

State-of-the-Art control strategies for robotic PiH assembly

Nowadays, industrial robots have been widely applied for performing position-controlled tasks with minimum contact such as spot welding, spray painting, packing, and material handling; however, performing high-tolerance assembly tasks still poses a great challenge for robots because of various uncertainties of the parts to be assembled such as fixtures, end effector tools, or axes. From this perspective, the advancement of research and development has led to cutting-edge robotic technologies for industrial applications. To understand the technological trend of industrial robots, investigated the state-of-the-art robotic assembly technologies to identify the limitations of existing works and clarify future research directions in the field. This paper especially interested on typical peg-in-hole (PiH) assemblies, as PiH methods provide insights for further development of robot assemblies. The assembly control strategies for PiH operations is classified by based on the types and features of the assemblies, and the literature in terms of the contributions of these studies is compared to PiH assembly. Finally, the control strategies for robotic PiH assemblies are discussed in detail, and the limitations of the current robotic assembly technologies are discussed to identify the future direction of research for the control of robotic assembly.     

Augmented reality-assisted robot programming system for industrial applications

Robots are important in high-mix low-volume manufacturing because of their versatility and repeatability in performing manufacturing tasks. However, robots have not been widely used due to cumbersome programming effort and lack of operator skill. One significant factor prohibiting the widespread application of robots by small and medium enterprises (SMEs) is the high cost and necessary skill of programming and re-programming robots to perform diverse tasks. This paper discusses an Augmented Reality (AR) assisted robot programming system (ARRPS) that provides faster and more intuitive robot programming than conventional techniques. ARRPS is designed to allow users with little robot programming knowledge to program tasks for a serial robot. The system transforms the work cell of a serial industrial robot into an AR environment. With an AR user interface and a handheld pointer for interaction, users are free to move around the work cell to define 3D points and paths for the real robot to follow. Sensor data and algorithms are used for robot motion planning, collision detection and plan validation. The proposed approach enables fast and intuitive robotic path and task programming, and allows users to focus only on the definition of tasks. The implementation of this AR-assisted robot system is presented, and specific methods to enhance the performance of the users in carrying out robot programming using this system are highlighted.     

Adaptive motion generation using imitation learning and highly compliant end effector for autonomous cleaning

Recent demographic trends in super aging societies, such as Japan, is leading to severe worker shortage. Service robots can play a promising role to augment human workers for performing various household and assistive tasks. Toilet cleanup is one such challenging task that involves performing complaint motion planning in a constrained toilet setting. In this study, we propose an end-to-end robotic framework to perform various tasks related to toilet cleanup. Our key contributions include the design of a complaint and multipurpose end-effector, an adaptive motion generation algorithm, and an autonomous mobile manipulator capable of garbage detection, garbage disposal and liquid removal. We evaluate the performance of our framework with the competition setting used for toilet cleanup in the Future Convenience Store Challenge at the World Robot Summit 2018. We demonstrate that our proposed framework is capable of successfully completing all the tasks of the competition within the time limit.     

自主机器人的强化学习研究进展

虽然基于行为控制的自主机器人具有较高的鲁棒性,但其对于动态环境缺乏必要的自 适应能力．强化学习方法使机器人可以通过学习来完成任务,而无需设计者完全预先规定机 器人的所有动作,它是将动态规划和监督学习结合的基础上发展起来的一种新颖的学习方法 ,它通过机器人与环境的试错交互,利用来自成功和失败经验的奖励和惩罚信号不断改进机 器人的性能,从而达到目标,并容许滞后评价．由于其解决复杂问题的突出能力,强化学习 已成为一种非常有前途的机器人学习方法．本文系统论述了强化学习方法在自主机器人中的 研究现状,指出了存在的问题,分析了几种问题解决途径,展望了未来发展趋势．     

CAMPOUT: a control architecture for tightly coupled coordination of multirobot systems for planetary surface exploration

Exploration of high risk terrain areas such as cliff faces and site construction operations by autonomous robotic systems on Mars requires a control architecture that is able to autonomously adapt to uncertainties in knowledge of the environment. We report on the development of a software/hardware framework for cooperating multiple robots performing such tightly coordinated tasks. This work builds on our earlier research into autonomous planetary rovers and robot arms. Here, we seek to closely coordinate the mobility and manipulation of multiple robots to perform examples of a cliff traverse for science data acquisition, and site construction operations including grasping, hoisting, and transport of extended objects such as large array sensors over natural, unpredictable terrain. In support of this work we have developed an enabling distributed control architecture called control architecture for multirobot planetary outposts (CAMPOUT) wherein integrated multirobot mobility and control mechanisms are derived as group compositions and coordination of more basic behaviors under a task-level multiagent planner. CAMPOUT includes the necessary group behaviors and communication mechanisms for coordinated/cooperative control of heterogeneous robotic platforms. In this paper, we describe CAMPOUT, and its application to ongoing physical experiments with multirobot systems at the Jet Propulsion Laboratory in Pasadena, CA, for exploration of cliff faces and deployment of extended payloads.     

From pixels to multi-robot decision-making: A study in uncertainty

Mobile robots must cope with uncertainty from many sources along the path from interpreting raw sensor inputs to behavior selection to execution of the resulting primitive actions. This article identifies several such sources and introduces methods for (i) reducing uncertainty and (ii) making decisions in the face of uncertainty. We present a complete vision-based robotic system that includes several algorithms for learning models that are useful and necessary for planning, and then place particular emphasis on the planning and decision-making capabilities of the robot. Specifically, we present models for autonomous color calibration, autonomous sensor and actuator modeling, and an adaptation of particle filtering for improved localization on legged robots. These contributions enable effective planning under uncertainty for robots engaged in goal-oriented behavior within a dynamic, collaborative and adversarial environment. Each of our algorithms is fully implemented and tested on a commercial off-the-shelf vision-based quadruped robot.     

Optimal design of neural networks for control in robotic arc welding

Robotic gas metal arc (GMA) welding is a manufacturing process which is used to produce high quality joints and has to a capability to be utilized in automation systems to enhance productivity. Despite its widespread use in the various manufacturing industries, the full automation of the robotic GMA welding has not yet been achieved partly because mathematical models for the process parameters for a given welding tasks are not fully understood and quantified. In this research, an attempt has been made to develop a neural network model to predict the weld bead width as a function of key process parameters in robotic GMA welding. The neural network model is developed using two different training algorithms; the error back-propagation algorithm and the Levenberg–Marquardt approximation algorithm. The accuracy of the neural network models developed in this study has been tested by comparing the simulated data obtained from the neural network model with that obtained from the actual robotic welding experiments. The result shows that the Levenberg–Marquardt approximation algorithm is the preferred method, as this algorithm reduces the root of the mean sum of squared (RMS) error to a significantly small value.     

Design Issues for Therapeutic Robot Systems: Results from a Survey of Physiotherapists

Over the years, research into rehabilitation robots has increased considerably. Using robots for rehabilitation can improve persons with physical disabilities to perform the basic activities of daily living. However, rehabilitation robots are not welcome yet in clinical environments. While surveys concerning how patients respond to robots used for rehabilitation have been conducted, no survey exists in the literature concerning how the therapists themselves think of these robotic devices, and what functionality they should possess in order to be effective. This paper presents a survey of physiotherapists concerning their thoughts, experience, and what functionality should be included in robots used for rehabilitation. In particular, the therapists were asked about the development of an intelligent robotic device capable of performing repetitive tasks for patients who suffer from reduced upper and lower limb mobility. In general, the results from this survey suggest that therapists respond positively to the idea of robotic devices in a clinical setting. Furthermore, the majority of respondents are interested in rehabilitation robotics. The results of the survey will be very helpful in the design of robotic systems for use during physiotherapy.