Colour sorting ROS-based robot evaluation under different lights and camera angles

We present software for interfacing FITPix and USB Lite compatible readout electronics using Robot Operating System (ROS). ROS is widely adopted middleware for integration of sensors, processing algorithms and logic to autonomous systems such as robots, unmanned helicopters, and robotic payloads. Thanks to ROS, Timepix detectors can be deployed in automated experiments on platforms spanning from traditional desktop computers to small ARM devices such as Raspberry Pi and Odroid. Acquisition and detector settings can be controlled in Linux shell allowing deployment on headless devices. Using ROS networking capabilities, captured frames and detector control can be transmitted via a network, which allows building distributed processing pipelines. The proposed software is a lightweight package, easily connectable to existing visualization, logging, and processing software in ROS. It offers simple bindings to custom Python and C++ programs for real-time control of the acquisition or processing of the captured frames. Rospix was deployed on NASA suborbital rocket, which was successfully launched on April 4, 2018, from Kwajalein Atoll by Pennsylvania State University. Rospix provides reliable solution for mobile robots where the collected data are used in real time to guide the robot through an environment. We release Rospix using the GitHub platform and welcome the community to contribute on the first project connecting the fields of ionizing radiation imaging and mobile robotics.

Tracking a specified target is a fundamental task of service robots. This paper studies and designs a monocular mobile robot tracking system based on Fully-Convolutional Siamese Networks. This tracking mobile robot system uses the Siamese convolution network as the tracker to lock the target. The bearing conversion algorithm and speed conversion algorithm are combined to implement following the target stably and accurately. The bearing conversion algorithm keeps the tracking target in the middle of the image and calculates the moving angle according to the distance from the center. The speed conversion algorithm sets different speeds according to the ratio of bounding box area. Implementation of the monocular mobile robot tracking system is grounded on the Robotic Operating System (ROS) platform. The experiment shows the effectiveness of the implemented the monocular robot tracking system which can achieve real-time following target at variable speeds according to the forward speed of the tracking target with safety distance even though having occlusion.

Significant research issues and experimental possibilities on autonomous vehicles are in vogue around the world. Among these, collision-free navigation has constituted one of the significant fields to research. Along with the vigorous emergence of definite sensors, challenges are coming in new avenues. This paper proposes a keen way to detect on-route obstacles using training of model through adversarial neural network along with 3D reconstruction of a surrounding under GPS-denied Indoor Environment (IE) using point cloud map. The depth sensor used here has been systematically analysed to tackle the challenge. This paper also studies the possible challenges and hurdles faced by the customized mobile robot to create point cloud map using depth sensor integrated with Robot Operating System (ROS) platform to reconstruct the surrounding. The reconstructed visuals been used as memory trail of environment along with visual reference. This array of references would prove to be credential for path planning algorithm and promises finding near optimal collision-free Indoor Mobile Robot Navigation (IMRN).KeywordsAdversarial neural network3D reconstructionPoint cloud mapIndoor Mobile Robot Navigation

With the advancement of intelligent manufacturing and flexible automation, mobile industrial robots are increasingly being deployed in scenarios such as material handling, inspection, and collaborative operations. As one of the core technologies for mobile robots, the localization system has a direct impact on the stability and accuracy of path planning and task execution. This paper, built on the Robot Operating System (ROS) platform, systematically reviews and analyzes the implementation mechanisms and applicable scenarios of mainstream localization algorithms, with a focus on the performance characteristics of the Extended Kalman Filter (EKF) and Adaptive Monte Carlo Localization (AMCL) in industrial settings. By constructing an experimental platform that fuses multiple sensors—lidar, IMU, and wheel odometry—a series of tests comparing localization accuracy and robustness are conducted. We evaluate each algorithm's adaptability to complex conditions including dynamic occlusions, uniform environmental textures, and multipath interference. The results indicate that AMCL achieves higher positioning accuracy in static, structured environments, whereas EKF is better suited to dynamic applications suffering from sensor drift and data latency. Finally, we propose an optimization approach that integrates visual SLAM and deep-learning–based feature extraction, offering guidance for designing highly reliable localization systems for future industrial robots.

The construction of a smart autonomous mobile robot system that can operate in an anonymous environment can be difficult. Building an autonomous mobile robot requires collecting data from sensors and cameras in order to recognize the surrounding environment, get knowledge of existing landmarks, and figure out its position and orientation. Many research studies have been carried out in recent years providing algorithms for achieving autonomous motion of mobile robot systems and object detection. The most common platform utilized for robot motion design is ROS (Robot Operating System) and for object detection YOLO (You Only Look Once) is a common choice. Additionally, the autonomous system needs motion planning in a systematic way to avoid obstacles by using LIDAR or other sensing methods generating the feedback signals required for control of motion around occupied spaces. One of the most effective and versatile feedback systems for mobile robot control can be constructed using visual feedback, which relies on a high-resolution camera that processes the image in real-time and the result is fed back to the control system for decision making. Visual feedback can be employed alone without sensors for feedback control of autonomous mobile robots, and it can be used for multiple tasks like path planning, obstacle avoidance, object detection, object recognition in addition to motion control. In this research, results of design and construction of an autonomous mobile rescue robot for finding victims through video data are presented. It is aimed to find victims via applying video as a data collection and feedback generation tool in the autonomous mobile robot control system. The visual feedback and YOLO v3 are used not only for finding victims but also for motion control. Our autonomous mobile robot is designed to move smoothly to the victim's direction and avoids obstacles by using ROS Global Planner. The robot is tested in multiple scenarios in different locations, various obstacle and victim numbers, and satisfactory results with good performances are obtained.

This research examines how humans work with tele-operated unmanned mobile robots to perform interaction to do the inspection in industrial plant area to produce a 3D map for further critical evaluation. This experiment focuses on two parts, the way human-robot doing the remote interactions using robust method and the way robot perceives its surrounding environment as a 3D perspective map. Robot operating system (ROS) as a tool was utilized in the development and implementation of this research which comes up with robust data communication method in the form of messages and topics. RGBD simultaneous localization and mapping (SLAM) performs the visual odometry and mapping function simultaneously to construct 3D map using Kinect sensor. The results showed that the mobile robot-based tele-operated system utilizing RGBD SLAM for 3D inspection task under ROS environment are successful in extending human perspective in term of remote surveillance in the large area of the industrial plant.

In this work, we tested Simultaneous localization and mapping (SLAM) about mobile robots in indoor environment, where all experiments were conducted based on the Robot Operating System (ROS). The urban search and rescue(USAR) environment was build in the ROS simulation tool Gazebo, and our car was used to test hector SLAM in Gazebo. The rplidar A1 single-line lidar was used for 2D laser scan matching data acquisition in the practical experiments and the indoor map was built by using the open source algorithms gmapping, karto SLAM, hector SLAM software package for indoor SLAM, which can get the indoor grid maps in ROS graphical tool RVIZ. The experimental results of the three open source algorithms show that the mobile robot for simultaneous localization and mapping (SLAM) is feasible, and high-precision grid maps can be constructed.

For a person following mobile robot, identifying the specific host to follow is challenging in presence of other people with similar features in its camera frame or field of view. In this paper we propose the use of Pedestrian Dead-Reckoning using the Inertial Measurement Unit (IMU) data from smartphone of host or user, which is used to localize the user in the map frame of the mobile robot. The movement and orientation of the user is used to calculate the path of the user and used as global targets for the mobile robot to follow. The mobile robot which is doing Simultaneous Localization and Mapping (SLAM), running on the Robot Operating System (ROS) framework follows the user as the user moves while it also implements obstacle avoidance locally. Since the smartphone is uniquely paired with the mobile robot, the ambiguity of identifying the host is also solved.

Research on mobile robots began in the late 1960s. Mobile robots are a typical autonomous intelligent system and a hot spot in the high-tech field. They are the intersection of multiple technical disciplines such as computer artificial intelligence, robotics, control theory and electronic technology. The product not only has potentially very attractive application value and commercial value, but the research on it is also a challenge to intelligent technology. The development of mobile robots provides excellent research for various intelligent technologies and solutions. This dissertation aims to study the research of multi-sensor information fusion and intelligent optimization methods and the methods of applying them to mobile robot related technologies, and in-depth study of the construction of mobile robot maps from the perspective of multi-sensor information fusion. And, in order to achieve this function, combined with autonomous exploration and other related theories and algorithms, combined with the Robot Operating System (ROS). This paper proposes the area equalization method, equalization method, fuzzy neural network and other methods to promote the realization of related technologies. At the same time, this paper conducts simulation research based on the SLAM comprehensive experiment of the JNPF-4WD square mobile robot. On this basis, the high precision and high reliability of robot positioning are further realized. The experimental results in this paper show that the maximum error of the X-axis and Y-axis, FastSLAM algorithm is smaller than EKF algorithm, and the improved FASTSALM algorithm error is further reduced compared with the original FastSLAM algorithm, the value is less than 0.1.

Mobile robot is an important part of robotics. Along with the rapid progress of science and technology, human society has more and more strong demand for mobile robots. The environment which the robot faces has become increasingly complex, and robots' capability of obstacle avoidance is expected at a higher level of requirements. This paper describes map building and global path planning when the mobile robot moves in a 2D plane. Firstly, the thesis summarizes the methods of map building and path planning on mobile robot and analyzes the advantages and disadvantages of these methods. Secondly, according to the needs of high-accuracy map building and high-performance path planning in practice, we use the method called "Map Forgetting" to increase mapping accuracy and take D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm to ensure the planning efficiency. Finally, the program architecture from information perception to base controller based on the robot operating system (ROS) is established. We build simulation platform and entity platform based for testing. The experiments show that the mapping accuracy and programming efficiency satisfy design requirements.

One of the challenges of outdoor robots is developing effective portable Human-Robot-Interaction (HRI) frameworks. Hand-held devices offer a practical solution. By equipping these devices with robot software, they can be made to interact with the outdoor robots. Android devices are ideal as they are open source and can be integrated with robots powered by the Robot Operating System (ROS), also open source. However, due to the limits of rosjava, the mechanism that links ROS with android, and the conflicting modes of operation between ROS and android, current implementations of ROS-android offer limited robot applications that do not support advanced operations such as autonomous navigation and others. This paper implements selective compartmentalization to overcome these limitations, by combining ROS with android through a number of ROS and android bridges that would facilitate the development of advanced robot applications. Through the proposed method, authors were able to develop a portable HRI framework that allowed human operators to supervise an outdoor mobile robot while it performed an autonomous task. From their mobile devices, users were able to initialize the robot, configure its motion, and monitor its progress. Also, users were able to reprogram the robot to perform new tasks (not previously planned) through a creative use of features offered in the developed HRI framework. Also, user cognitive effort was reported to be low as evident by the positive score on the NASA-TLX scale test which was corroborated with robot performance data. This paper presents the detailed development and implementation steps.

For a mobile service robot, safe navigation is essential. To do so, the robot needs to be equipped with collision avoidance and global path planning capabilities. The target platforms considered in this paper are Ackermann-driven vehicles. Hence, a planning approach which directly takes the kinematic and dynamic constraints of the vehicle into account is required. The Search-based Planning Library (SBPL) package of the Robot Operating System (ROS) provides global path planning which takes these constraints into account. However, it misses a local planner that can also make use of the Ackermann kinematic constraints for collision avoidance. A local planner is useful to take dynamic obstacles into account as early as possible. In this paper, we extend the SBPL included in ROS by a local planner, which makes use of motion primitives. We extend the ROS package and show first experimental results on Ackermann-driven vehicles.

In this study, a differential wheeled mobile robot was controlled in real time using pure pursuit algorithm (PPA). The robot was obtained in a simulation environment by using Gazebo simulator which offer the ability to accurately and efficiently simulate various robots in complex indoor/outdoor environments. This simulator was operated with robot operating system (ROS) that allows the use of Python, C++, MATLAB or various programming languages. In this paper, MATLAB/Simulink environment was used to control the robot with communication interface between MATLAB and ROS. Thus, it is possible to study more comprehensively by using multiple the features of MATLAB. The robot was traveled around a 4m x 4m area with random waypoints. The position of the robot was measured based on odometer sensor in order to determine the robot’s location. The performance of the control algorithm was analyzed using various information of the robot such as robot’s velocity, motors’ speed, the position of the robot, etc.

Aiming at the problem of positioning and navigation of mobile robots indoors, this paper designs and implements related functions based on Robot Operating System and laser radar. The core controller of the robot adopts an industrial computer, and is equipped with a laser radar to acquire information about the surrounding environment and develop related algorithms on the Robot Operating System. Realize Simultaneous Localization and Mapping function based on Cartographer algorithm, path planning based on optimal path algorithm and navigation function based on particle filter algorithm. The real machine experiment proves that the system can efficiently perform real-time synchronous positioning and map construction and navigation functions.

We present the successful demonstration of autonomous robot KUKA YouBot navigation based on path planning and traffic signals recognition. The integration of both capabilities path planning and traffic signals recognition was carried out, thanks to the integration among Robot Operating System, MATrix LABoratory software and Open Source Computer Vision Library working environments. The Robot Operating System allows the simulation of the autonomous robot navigation by using Gazebo and provides the implementation of the algorithms in simulated and real platforms. MATrix LABoratory software improves the communication tasks taking advantage of data processing tools in the path planning process. Finally, Open Source Computer Vision Library allows the traffic signals recognition by using the Scale-Invariant Feature Transform and Speeded-Up Robust Features algorithm. The integration of Robot Operating System, MATrix LABoratory software and Open Source Computer Vision Library is a promising approach to provide autonomous navigation capability in any mobile robot and in uncontrolled environments.