Kumpulan Paper Keren

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• [x] Design and Simulation of an Embedded Controller for the iRobot Create robot by Integrating ROS in Gazebo, pdf.
• [ ] Robot Operating System (ROS) dan Gazebo Sebagai Media Pembelajaran Robot Interaktif, web, pdf.
• [x] Cyberbotics Ltd. Webots™: Professional Mobile Robot Simulation, web.
• [ ] V-REP: A versatile and scalable robot simulation framework, web.
• [ ] Comprehensive Simulation of Quadrotor UAVs Using ROS and Gazebo, web.
• [ ] Manipulation task simulation using ROS and Gazebo, web.
• [ ] Transportable open-source application program interface and user interface for generic humanoids: TOUGH, web.
• [ ] Unmanned Ground Vehicle Modelling in Gazebo/ROS-Based Environments, web.
• [x] Configuration and Control of KMR iiwa Mobile Robots using ROS2, IEEE Xplore, PDF.
• [x] A Self-Driving Car Architecture in ROS2, IEEE Xplore, PDF.
• [x] Exploring the performance of ROS2, IEEE Xplore, PDF.
• [ ] ROS: an open-source Robot Operating System, PDF.
• [ ] OMG Data-Distribution Service: Architectural Overview, IEEE Xplore, PDF.
• [ ] OMG data-distribution service (DDS): architectural update, IEEE Xplore, PDF.
• [ ] Implementation of PLC controller connected Gazebo-ROS to support IEC 61131-3, IEEE Xplore, PDF.
• [x] ROS-Based Multi-Robot System Simulator, IEEE Xplore, PDF.
• [x] ROS based service robot platform, IEEE Xplore, PDF.
• [x] Intelligent Hotel ROS-based Service Robot, IEEE Xplore, PDF.
• [ ] Comparison of ROS-based visual SLAM methods in homogeneous indoor environment, IEEE Xplore, PDF.
• [ ] An Industrial Robotics Application with Cloud Computing and High-Speed Networking, IEEE Xplore, PDF.
• [ ] Design and Development of a ROS Enabled CAN Based All-Terrain Vehicle Platform, IEEE Xplore, PDF.
• [ ] ZytleBot: FPGA integrated development platform for ROS based autonomous mobile robot, IEEE Xplore, PDF.
• [ ] Deep learning for object identification in ROS-based mobile robots, IEEE Xplore, PDF.
• [ ] Using AADL to Model and Develop ROS-Based Robotic Application, IEEE Xplore, PDF.
• [ ] Real-Time ROS Extension on Transparent CPU/GPU Coordination Mechanism, IEEE Xplore, PDF.
• [ ] A novel solution for securing robot communications based on the MQTT protocol and ROS, IEEE Xplore, PDF.
• [ ] ROS Based, Simulation and Control of a Wheeled Robot using Gamer’s Steering Wheel, IEEE Xplore, PDF.
• [ ] Development of a wireless communication platform for multiple-mobile robots using ROS, IEEE Xplore, PDF.
• [ ] Erle-copter Simulation using ROS and Gazebo, IEEE Xplore, PDF.
• [ ] Simulation of Autonomous Airship on ROS-Gazebo Framework, IEEE Xplore, PDF.
• [ ] Research on Motion Planning System of Service Robot Based on ROS, IEEE Xplore, PDF.
• [ ] General simulation platform for vision based UAV testing, IEEE Xplore, PDF.
• [ ] Keyboard-Based Control and Simulation of 6-DOF Robotic Arm Using ROS, IEEE Xplore, PDF.
• [ ] Vision based control and simulation of a spherical rolling robot based on ROS and Gazebo, IEEE Xplore, PDF.
• [ ] A high fidelity simulator for a quadrotor UAV using ROS and Gazebo, IEEE Xplore, PDF.
• [ ] Object Detection Algorithms and Implementation in a Robot of Service, IEEE Xplore, PDF.
• [ ] Discrete-time formation and marching control of Multi-Agent Robots Systems, IEEE Xplore.
• [ ] Working with Machines: The Impact of Algorithmic and Data-Driven Management on Human Workers, ACM Digital Library.
• [ ] Deep Detection of People and their Mobility Aids for a Hospital Robot, IEEE Xplore.
• [ ] Incorporating artificial intelligence in shopping assistance robot using Markov Decision Process, IEEE Xplore.
• [ ] Intelligent Mobile Robot Controller Design for Hotel Room Service with Deep Learning Arm-Based Elevator Manipulator, IEEE Xplore.
• [ ] Path Planning for Virtual Human Motion Using Improved A* Star Algorithm, IEEE Xplore.
• [ ] On the Reaction to Robot’s Speech in a Hotel Public Space, Springer Link.
• [x] Learning to Collaborate From Simulation for Robot-Assisted Dressing, IEEE Xplore, PDF.
• [ ] Development of a High-Fidelity Robot-Assisted Kidney Transplant Simulation Platform Using Three-Dimensional Printing and Hydrogel Casting Technologies, Liebert Pub.
• [x] A Framework for Realizing Industrial Human-Robot Collaboration through Virtual Simulation, Science Direct.
• [ ] From Crowd Simulation to Robot Navigation in Crowds, IEEE Xplore.
• [ ] Design of a Carangiform Swimming Robot through a Multiphysics Simulation Environment, MDPI.
• [ ] Optimal Nonlinear PID Control of a Micro-Robot Equipped with Vibratory Actuator Using Ant Colony Algorithm: Simulation and Experiment, Springer Link.
• [ ] Lio-A Personal Robot Assistant for Human-Robot Interaction and Care Applications, IEEE Xplore.
• [ ] Improved Human–Robot Collaborative Control of Redundant Robot for Teleoperated Minimally Invasive Surgery, IEEE Xplore.
• [ ] Gym-Ignition: Reproducible Robotic Simulations for Reinforcement Learning, IEEE Xplore.
• [ ] Design of Obstacle Avoidance System on Hexacopter Using Vector Field Histogram-Plus, IEEE Xplore.
• [ ] Autonomous Tracking of Hexacopter on Moving Mobile Robot Using Gazebo ROS Simulation, ACM Digital Library.
• [ ] Automatic tool for Gazebo world construction: from a grayscale image to a 3D solid model, IEEE Xplore.
• [ ] ChoiRbot: A ROS 2 Toolbox for Cooperative Robotics, arXiv.
• [ ] gym-gazebo2, a toolkit for reinforcement learning using ROS 2 and Gazebo, arXiv
• [ ] Rollin' Justin - Design considerations and realization of a mobile platform for a humanoid upper body, IEEE Xplore.
• [ ] Transfer Learning for Robot Control with Generative Adversarial Networks, PDF.
• [ ] Deep Learning for Autonomous Control of Robot’s Flippers in Simulation, PDF.

Referensi

• Topik tugas akhir pak muhtadin, slide
• Bold Heart, Hands-on With ROS 2, GitLab
• IEEE Xplore, ROS2, ROS, service robots.

Scope

• Pengurangan resiko
• Replikasi environment yg tidak mungkin
• Pengurangan biaya

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Configuration and Control of KMR iiwa Mobile Robots using ROS2

• Sources: IEEE Xplore, PDF
• Bahas bagaimana mengontrol KMR iiwa secara remote pake ROS 2 (KMR iiwa punya cara sendiri buat diprogram)
• "In this paper, we present a communication interface between the KMR iiwa and ROS2 to control the mobile platform. The developed system integrates with the Sunrise.OS operating system of the mobile robot and exposes sensor and control interfaces over UDP, User Datagram Protocol, and TCP sockets."
• Batasannya membuat program untuk KMR iiwa berbasis ROS 2 dengan Java
• Pengujian dilakukan dengan mencoba modul Cartographer dan Navigation2
• "By creating separate nodes and connections for all tasks, it is possible to launch only the nodes needed for a specific use case. This limits the transferred data and enables a faster system."
• kerjaan di open di repo github

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A Self-Driving Car Architecture in ROS2

• Sources: IEEE Xplore, PDF
• "The second generation of Robot Operation System ROS2 provides the needed reliability and real-time performance while most of the advantages of ROS1 are still available."
• "As expected the measurements show a much better real-time performance of the system when using the real-time kernel, but it also shows that just using ROS2 is not enough."
• "Vehicles have to be moved at slow speed for, say, parking in an underground parking garage. In those situations the GNSS sensor often gives no suitable information and the localisation has to be realised by other sensor systems such as Lidar. These sensor systems measure point clouds, which may require a longer processing time. But due to the slower speed, also more time is available and more accurate localisation methods can be used, such as Monte Carlo localisation."

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Exploring the performance of ROS2

• Sources: IEEE Xplore, PDF
• "The Robot Operating System (ROS) is open-source middleware that has undergone rapid develop-ment and has been widely used for robotics applications (e.g., autonomous driving systems)."
• "However, the ROS does not satisfy real-time run require-ments and only runs on a few OSs. In addition, the ROS cannot guarantee fault-tolerance, deadlines, or process syn-chronization."
• "ROS2 will consider the following new use cases: real-time systems, small embedded platforms (e.g., sensor nodes), non-ideal networks, and cross-platform (e.g., Linux, Windows, Mac, Real-Time OS (RTOS), and no OS)."
• "The ROS1 transport system will be replaced by DDS, an industry-standard real-time communication system and end-to-end middleware."
• "DDS meets the requirements of distributed systems for safety, resilience, scalability, fault-tolerance and security. DDS can provide solutions for some real-time environments and some small/embedded systems by reducing library sizes and memory footprints."
• "The core of DDS is a Data-centric publish-subscribe (DCPS) model designed to provide efficient data transport between processes even in distributed heterogeneous platforms."
• "After this transaction, a Data Writer connects to a DataReader using the Real-Time Publish/Subscribe (RTPS) protocol in distributed systems."
• "So far, we have clarified characteristic of DDS implementations through ROS2 from several standpoints: ROS2 capability, latencies, throughput, the number of threads and memory consumption."
• "In addition to the ROS, the Robot Technology Middleware (RTM) is well known and widely used for robotics development."
• Tidak melakukan percobaan ROS 2 untuk real time pada RTOS maupun percobaan ROS 2 untuk embedded system.

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ROS-Based Multi-Robot System Simulator

• Sources: IEEE Xplore, PDF
• "The simulator runs under the ROS framework can both simulate and control the real robots with minor modification, therefore, the ROS-based MRS simulator is much more efficient for theoretical verification of coordination protocols of MRS."
• "As an open source robot operating system, ROS provides a communication layer and a data and programming structure. It is a collection of tools, libraries, and conventions that aim to simplify the task of creating complex and robust robot behavior across a wide variety of robotic platforms."
• Pembahasan lebih ke persoalan control system pada multi robot system (MRS).
• "However, for the sake of simplicity, some researchers take the robots as mass points when they do some theoretical researches in MRS coordination problems [8]."
• "The GUI of the simulator with the ROS environment is similar to the open source library interacting between ROS and the real robot. Therefore, the simulator reduces the gap between numerical simulation and full hardware experimentation. It will be an effective simulation tool for the theoretical researchers."
• Pengembangan lebih lanjut arahnya terhadap modifikasi robot langsung pada GUI, penggunaan model robot lain seperti manipulator, quadrotor, dan bentuk koordinasi lain seperti flocking dan marching.

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ROS based service robot platform

• Sources: IEEE Xplore, PDF
• "Human robot interaction has become one of the emerging field in robotics in the recent years [1-4] and with growing technology, it is expected that service robots fulfill these requirements appropriately in a multitude of areas where a human factor can be easily substituted by a minimal error robotic platform."
• "But for this to be possible the robot must be able to navigate in its operating environment autonomously which calls for another prerequisite process of map building of these unknown areas and localizing itself, which has been considered a fundamental problem in mobile robotics [5]."
• "In this respect a number of low cost and reliable solutions have been developed by the use of low cost RGB-D sensors such as the Microsoft Kinect [6,7,8,9]."
• (ROS nodes communication via topics) "This is an example of a decoupled system which can be used as different parts of the robot performing different functions without hampering each other."
• "The other advantage of using ROS is that the code can be reused [11] so that research can be further extended from already existing code instead of reinventing the wheel."
• "The Kinect helps here. It is a low cost 3D RGB-D sensor i.e. it provides both the depth data along with the color image. It has an IR emitter and an IR depth sensor that makes it capable of capturing depth information too [12]."
• _"This paper uses an RGB-D graph based SLAM approach, called RTAB-Map (Real-Time Appearance Based Mapping)[14] by using a ROS package called rtabmap_ros. RTAB-Map provides an online loop closure solution i.e. it recognizes already mapped locations in real time constraints."_
• _"RTAB-Map also allows for multi-session mapping which can be used to solve the kidnapping problem, which means, if the robot is picked up and placed elsewhere, a map is started and whenever a common location with the old map is found which causes a loop closure, the maps are joined."_
• "It uses pre-existing 2D maps for the purpose of localization and is based on Bayesian probabilistic localization and uses particle filters[16,17,18] which are actually sampling methods[16] ."
• "The particle filter used for Adaptive Monte Carlo Localization is called KLD (Kullback-Leibler divergence) sampling. KLD sampling is the reason for the term adaptive that is used in Adaptive Monte Carlo Localization."
• Batasannya membuat robot yang bekerja dengan sensor kinect yang murah, dan melakukan lokalisasi berbasis adaptive monte carlo localization dan navigasi ke suatu target, hasilnya diuji cobakan dan ditampilkan berdasarkan visualisasi di Rviz.

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Intelligent Hotel ROS-based Service Robot

• Sources: IEEE Xplore, PDF
• "The birth of modern robots marks the beginning of the realization of the human dream of intelligent machines [1]."
• "Nowadays, with the development of the IoT (Internet of Things) [3], various types of robots are applied to carry out different tasks and in different applications. Therefore, robots are used at hotels, shopping malls, hospitals and many others. [4, 5, 6]."
• "After check in, the G-mapping manager can plan the nearest route to the destination depending on the room number typed on the digital keyboard."
• "Using pioneer-3dx robot combined with Lidar, Kinect camera and speaker (as shown in figure 1), intelligent hotel robot is constructed."
• "We developed the navigation and G-mapping, combining with the MATLAB, ROS and Eclipse."
• "The route setting is based on D* arithmetic algorithm. This algorithm is based on iteratively selecting a node from the open list."
• "In the navigation of ROS, the global route of the robot to the target location is first calculated through global path planning. This feature is implemented by by the "navfn" package."
• "During the test process, we found that the error on the wheel odometry have a big influence on the total journey."
• Batasannya memrogram robot Pioneer 3dx yang dipasang HOKUYO Lidar untuk navigasi di lokasi dengan tingkat berbeda, hasilnya berupa waktu navigasi dan keberhasilan navigasi.
• Overall isinya jelek, intelligent voice system enggak dibahas, lengan pun juga tidak diimplementasikan di akhir.

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Learning to Collaborate From Simulation for Robot-Assisted Dressing

• Sources: IEEE Xplore, PDF
• "We investigated the application of haptic feedback control and deep reinforcement learning (DRL) to robot-assisted dressing. Our method uses DRL to simultaneously train human and robot control policies as separate neural networks using physics simulations."
• "It becomes ever more likely that robots will be found in homes and businesses, physically interacting with the humans they encounter. With this in mind, researchers have begun preparing robots for the physical interaction tasks which they will face in a human world."
• "These challenges are compounded by the risk of injuring people and the difficulty of recruiting participants, limiting the potential for robot exploration and learning. Learning from physics simulations presents a promising alternative that might enable robots to more freely explore and learn from vast amounts of data without putting people at risk."
• "Many typical self-dressing tasks can be challenging or insurmountable obstacles for individuals with limited capabilities. For these individuals, family members and/or paid nursing staff often provide daily supplemental care [2]."
• "Our work aims to model plausible dressing behavior for both humans and robots in simulated robot-assisted dressing tasks. We make two main contributions in our work."
  • "First, we introduce a policy model for human behavior that adapts to variation in a person’s physical capabilities."
  • "Second, we demonstrate a co-optimization method to simultaneously train collaborative policies for both a robot and a human using deep reinforcement learning (DRL) and curriculum learning."
• "Due in part to nursing shortages and increasing populations of older adults [2], robot-assisted dressing has seen a surge of research interest in recent years."
• "All of these methods take the approach of directly utilizing existing robotic platforms and sensors. Additionally, they learn or optimize models and control directly on real world data and expert demonstrations. In contrast, our work applies physics simulations for efficient, risk-free exploration of the task space."
• "Recent work by Kapusta et al. used simulations to search for collaborative dressing plans, consisting of sequences of alternating human and robot actions based on an individuals impairments [11]. In contrast, we sought collaborative feedback- control policies for robot-assisted dressing that can adapt to a range of impairments and allow the robot and the human to move at the same time."
• "Researchers have shown that a time series of end effector velocities and measurements from a gripper-mounted force torque sensor in simulation can be used to both classify sleeve dressing trial success and estimate force maps on the limb being dresse [12]–[14]. The resulting estimators can then be applied to physical systems to enable robotic dressing of a user with garments and static user configurations similar to those seen during training [15], [16]."
• "These methods demonstrate the potential for models trained in simulation to enable real robots to assist with dressing in the real world."
• "We formulate dressing tasks as Markov Decision Processes (MDPs) and solve them using DRL."
• "DRL has been applied largely to rigid-body control tasks in the complex domain of humanoid motor skills [17]–[19]. However, recent work by Clegg et al. has applied Trust Region Policy Optimization (TRPO) [20] to simulated human self-dressing [21]."
• "However, DRL for robot-assisted dressing in simulation has not been previously demonstrated with a full robot simulation, or simulated disabilities, nor have such policies been co-optimized for collaboration, or transferred to a real robot."
• "We take a co-optimization (concurrent learning) approach to simultaneous training of control policies for the human, π_θH , and robot, π_θR , modeled with fully-connected neural networks as shown in Fig. 2."
• "To model the behavior of the human when receiving dressing assistance, our method solves for a policy that outputs the optimal human action conditioned on the observation made by the human and the motor capability of the human."
• "Clegg et al. [21] proposed an observation space for learning a human self-dressing policy, including information about proprioception, haptics, surface information, and the task. We extend their observation space for robot-assisted dressing tasks by adding the 3D position of all robot and human joint positions, O_jp, and the target pose from the previous time step, O_tar."

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Moved here.