Importance and applications of robotic and autonomous systems (RAS) in railway maintenance sector: a review

Maintenance, which is critical for safe, reliable, quality, and cost-effective service, plays a dominant role in the railway industry. Therefore, this paper examines the importance and applications of Robotic and Autonomous Systems (RAS) in railway maintenance. More than 70 research publications, which are either in practice or under investigation describing RAS developments in the railway maintenance, are analysed. It has been found that the majority of RAS developed are for rolling-stock maintenance, followed by railway track maintenance. Further, it has been found that there is growing interest and demand for robotics and autonomous systems in the railway maintenance sector, which is largely due to the increased competition, rapid expansion and ever-increasing expense

Control strategies for cleaning robots in domestic applications: A comprehensive review:

Service robots are built and developed for various applications to support humans as companion, caretaker, or domestic support. As the number of elderly people grows, service robots will be in increasing demand. Particularly, one of the main tasks performed by elderly people, and others, is the complex task of cleaning. Therefore, cleaning tasks, such as sweeping floors, washing dishes, and wiping windows, have been developed for the domestic environment using service robots or robot manipulators with several control approaches. This article is primarily focused on control methodology used for cleaning tasks. Specifically, this work mainly discusses classical control and learning-based controlled methods. The classical control approaches, which consist of position control, force control, and impedance control , are commonly used for cleaning purposes in a highly controlled environment. However, classical control methods cannot be generalized for cluttered environment so that learning-based control methods could be an alternative solution. Learning-based control methods for cleaning tasks can encompass three approaches: learning from demonstration (LfD), supervised learning (SL), and reinforcement learning (RL). These control approaches have their own capabilities to generalize the cleaning tasks in the new environment. For example, LfD, which many research groups have used for cleaning tasks, can generate complex cleaning trajectories based on human demonstration. Also, SL can support the prediction of dirt areas and cleaning motion using large number of data set. Finally, RL can learn cleaning actions and interact with the new environment by the robot itself. In this context, this article aims to provide a general overview of robotic cleaning tasks based on different types of control methods using manipulator. It also suggest a description of the future directions of cleaning tasks based on the evaluation of the control approaches

Graphical User Interface (GUI) Controlled Mobile Robot

The advanced design and development of robotic technology in producing multi task are increasingly. In this paper presents about designing and developing mobile robot model that can be controlled using Graphical User Interface (GUI) via wireless protocol. This paper focuses on the control mobile robot by using the GUI as navigation control and the user can get a view an image and real time video on visual basic software. To address the problem of sired based control, XBee wireless communication circuit was used in mobile robot through a computer command. The development of this mobile robot consists of a chassis, a graphical user interface (GUI), XBee module, DC gear motor, camera, track wheels and microcontroller type PIC18F4550. Differential driving method using L298 circuit was used to control movement of the robot. In mechanical design, the wheel track has been used instead of conventional wheels to enable the robot to travel through different types of surfaces or rough terrain. In addition, wireless cameras was attached to the robot as a system of monitoring function. Finally, the robot will be designed to control wireless remote control that can control robots. Wireless remote control allows the user of an environment that is unsafe or dangerous device and evades wires or cables interfere with the movement of the robot

Cognition-enabled robotic wiping: Representation, planning, execution, and interpretation

Advanced cognitive capabilities enable humans to solve even complex tasks by representing and processing internal models of manipulation actions and their effects. Consequently, humans are able to plan the effect of their motions before execution and validate the performance afterwards. In this work, we derive an analog approach for robotic wiping actions which are fundamental for some of the most frequent household chores including vacuuming the floor, sweeping dust, and cleaning windows. We describe wiping actions and their effects based on a qualitative particle distribution model. This representation enables a robot to plan goal-oriented wiping motions for the prototypical wiping actions of absorbing, collecting and skimming. The particle representation is utilized to simulate the task outcome before execution and infer the real performance afterwards based on haptic perception. This way, the robot is able to estimate the task performance and schedule additional motions if necessary. We evaluate our methods in simulated scenarios, as well as in real experiments with the humanoid service robot Rollin’ Justin

Cognitive Reasoning for Compliant Robot Manipulation

Physically compliant contact is a major element for many tasks in everyday environments. A universal service robot that is utilized to collect leaves in a park, polish a workpiece, or clean solar panels requires the cognition and manipulation capabilities to facilitate such compliant interaction. Evolution equipped humans with advanced mental abilities to envision physical contact situations and their resulting outcome, dexterous motor skills to perform the actions accordingly, as well as a sense of quality to rate the outcome of the task. In order to achieve human-like performance, a robot must provide the necessary methods to represent, plan, execute, and interpret compliant manipulation tasks. This dissertation covers those four steps of reasoning in the concept of intelligent physical compliance. The contributions advance the capabilities of service robots by combining artificial intelligence reasoning methods and control strategies for compliant manipulation. A classification of manipulation tasks is conducted to identify the central research questions of the addressed topic. Novel representations are derived to describe the properties of physical interaction. Special attention is given to wiping tasks which are predominant in everyday environments. It is investigated how symbolic task descriptions can be translated into meaningful robot commands. A particle distribution model is used to plan goal-oriented wiping actions and predict the quality according to the anticipated result. The planned tool motions are converted into the joint space of the humanoid robot Rollin' Justin to perform the tasks in the real world. In order to execute the motions in a physically compliant fashion, a hierarchical whole-body impedance controller is integrated into the framework. The controller is automatically parameterized with respect to the requirements of the particular task. Haptic feedback is utilized to infer contact and interpret the performance semantically. Finally, the robot is able to compensate for possible disturbances as it plans additional recovery motions while effectively closing the cognitive control loop. Among others, the developed concept is applied in an actual space robotics mission, in which an astronaut aboard the International Space Station (ISS) commands Rollin' Justin to maintain a Martian solar panel farm in a mock-up environment. This application demonstrates the far-reaching impact of the proposed approach and the associated opportunities that emerge with the availability of cognition-enabled service robots