Implementation of Pure Pursuit Algorithm for Nonholonomic Mobile Robot using Robot Operating System

This paper describes a practical study of basic line follower wheeled mobile robot implemented during practical class for mechanical engineering students. The basic mobile robot from DIY KITS is used as an introductory-level differential wheeled mobile robot (DWMR) for students to get started in building mobile robot. The mini mobile robot controller, MC40A, combined with LSS05 auto-calibrate line sensor will develop the line follower DWMR. The sensor indication and DWMR wheel reaction is presented in this paper based on the black line follower mode. Several tracks are designed for multi tasking line follower DWMR to obtain the performance of line follower characteristics.

Developing an autonomous indoor mobile robot navigation system from scratch is very difficult and it takes a long time. It requires a series of complex tasks such as handling given sensor inputs, calculating all the information needed for autonomous navigation, and controlling actuators required for movement. In this paper, an autonomous navigation system for indoor mobile robots is introduced mainly based on open source provided by the robot operating system. The presented system is capable of autonomously navigating an unstructured indoor environment avoiding collision with static or dynamic objects. To this end, the system consists of three main modules: mapping, localization, and planning. The mapping module builds a global map for an unknown environment by means of a simultaneous localization and mapping algorithm based on laser scanner data. The localization module estimates the mobile robot’s pose within the prebuilt map by way of an adaptive Monte Carlo localization approach. The planning module builds a local cost map for collision avoidance, generates collision-free trajectories to reach a goal pose based on the cost map, and produces control commands to follow the trajectories. The presented system has been tested not only in simulation environments built in the Gazebo simulator but also in real environments utilizing the Jackal mobile robot, to validate its performance for autonomous navigation including collision avoidance.

Robots are an important part of urban search and rescue tasks. World wide attention has been given to developing capable physical platforms that would be beneficial for rescue teams. It is evident that use of multi-robots increases the effectiveness of these systems. The Robot Operating System (ROS) is becoming a standard platform for the robotics research community for both physical robots and simulation environments. Gazebo, with connectivity to the ROS, is a three-dimensional simulation environment that is also becoming a standard. Several simultaneous localization and mapping algorithms are implemented in the ROS; however, there is no multi-robot mapping implementation. In this work, two multi-robot mapping algorithm implementations are presented, namely multi-robot gMapping and multi-robot Hector Mapping. The multi-robot implementations are tested in the Gazebo simulation environment. Also, in order to achieve a more realistic simulation, every incremental robot movement is modeled with rotational and translational noise.

Abstract—This paper presents GAMYA, a cost-effective autonomous navigation vehicle developed using the Robot Operating System 2 (ROS2) framework. The system integrates a Raspberry Pi 4 as the central processing unit, an Arduino Uno R3 for motor control, and a 2D LiDAR sensor for environmental perception. GAMYA is designed to perform real-time mapping, localization, and obstacle avoidance through the implementation of the ROS2 Navigation2 stack and SLAM Toolbox. A differential drive mechanism is employed, and odometry is computed using encoder feedback to support accurate navigation. The system is tested in a simulated environment using Gazebo, where it successfully navigates dynamic and static obstacles. Results demonstrate reliable autonomous performance with minimal latency, showcasing the potential of using low-cost components to develop robust robotic platforms suitable for academic and research applications. Keywords—Autonomous Navigation, ROS2, LiDAR, SLAM, Gazebo Simulation, obstacle avoidance, Raspberry Pi, Arduino, Mobile Robot

PurposeThis study aims to introduce the fast reactive tree (FRT*) algorithm for enhancing replanning speed and reducing the overall cost of navigation in unknown dynamic environments.Design/methodology/approachFRT* comprises four key components: inverted tree build, convex hull construction, dead nodes inform activation and lazy-rewiring replanning. First, an initial path is found from the inverted tree where the valid structure is preserved to minimise re-exploration areas during the replanning phase. As the robot encounters environment changes, convex hulls are extracted to sparsely describe impacted areas. Next, the growth direction of the modified tree is biased by the inform activation of dead nodes to avoid unnecessary exploration. In the replanning phase, the tree structure is optimized using the proposed lazy-rewiring replanning to find a high-quality path with low computation burden.FindingsA series of comprehensive simulation experiments demonstrate that the proposed FRT* algorithm can efficiently replan short-cost feasible paths in unknown dynamic environments. The differential wheeled mobile robot with varying reference linear velocities is used to validate the effectiveness and adaptability of the proposed strategy in real word scenarios. Furthermore, ablation studies are conducted to analyze the significance of the key components of FRT*.Originality/valueThe proposed FRT* algorithm introduces a novel approach to addressing the challenges of navigation in unknown dynamic environments. This capability allows mobile robots to safely and efficiently navigate through unknown and dynamic environments, making the method highly applicable to real-world scenarios.

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.

The vehicle kinematics model based on Ackerman principle has a wide range of applications in the field of unmanned driving, and the motion planning control of autonomous mobile intelligent vehicles is of great significance for the research of unmanned driving. In this paper, the robot operating system (ROS) is used as the development platform to establish the simulation model of Ackerman intelligent vehicle, and the three-dimensional physical simulation software Gazebo is used to build the simulation environment. The improved rapid expansion random tree (RRT) algorithm is used as the global path planning algorithm, and the TEB algorithm is used for local path planning to complete the simulation verification of the motion planning and control of the Ackerman smart car. The simulation results show that the improved RRT algorithm can obtain the optimal global path, and its search efficiency and path smoothness are significantly improved. Ackerman intelligent vehicle uses this algorithm to move autonomously in the 3D physical simulation environment, and can avoid obstacles in real time, presenting a good simulation effect.

In this paper, two algorithms are proposed to navigate the mini unmanned aerial vehicle (MUAV) in unknown environment. The first algorithm is path planning algorithm, it proposed to force the MUAV to track a specific trajectory. The second algorithm is obstacle avoidance algorithm, it proposed to avoid the collision between the MUAV and borrower when the MUAV is maneuvering in unknown environment. The pure pursuit algorithm (PPA) is used to force the MUAV to follow the desired trajectory depends on the given waypoints. While the vector field histogram algorithm (VFHA) is used to avoid collision of the MUAV with obstacles block in the desired path. To keep the flight of MUAV without collision, the 3D laser sensors are used to collect the information from the surrounding environment. The GPS sensor is used to keep the MUAV on the specific path. The collected information from the two sensors are analysed by the controller of the MUAV to improve its performance. The Matlab program is used to implement the proposed algorithms and plot the trajectories of MUAV in the unknown algorithm. The Matlab program is connected with the gazebo program via robotic operating system (ROS) to simulate the visual movement of the MUAV in any unknown environment. The obtained results show the effectiveness of the proposed algorithm to flight the MUAV in different environments.

Human Robot Interaction (HRI) is one of the biggest research field in the Robotics research world. Understanding the semantic meaning of the user instruction is very important to establish the communication between user and robot. When a user instructs to all heterogeneous service Robots with high level instructions who are working at different locations in smart house, all robots need to operate by understanding the semantic of the instruction and complete the task uniformly without considering underline software and hardware implementations. Ontology is used to extract the semantic meaning of the instructions. Each robot is assigned a specific task for specific time period for each day. User can issue commands to all robots at different time slots but the same command can be issued with different sentence by different users. We have used ontology to represents the semantic of the sentence with different commands. We have used three robots (TurtleBot, TiaGo and Husky) for our experiment in Gazebo simulator. Robot Operating System (ROS) is the middleware which is hiding more complex implementation of different functions for different robots and provide the interoperability for heterogeneous robot operations. The ROS nodes and topics can be different for different robots, therefore our implementation can solve this issue by autonomous robot registration algorithm which is working with Robot ontology.

Autonomous navigation in dynamic environments presents a significant challenge for mobile robotic systems. This paper proposes a novel approach utilizing Convolutional Neural Networks (CNNs) for multi-object detection in 3D space and 2D segmentation using bird’s eye view (BEV) maps derived from 3D Light Detection and Ranging (LiDAR) data. Our method aims to enable mobile robots to localize movable objects and their occupancy, which is crucial for safe and efficient navigation. To address the scarcity of labeled real-world datasets, a synthetic dataset based on a simulation environment is generated to train and evaluate our model. Additionally, we employ a subset of the NVIDIA r2b dataset for evaluation in the real world. Furthermore, we integrate our CNN-based detection and segmentation model into a Robot Operating System 2 (ROS2) framework, facilitating communication between mobile robots and a centralized node for data aggregation and map creation. Our experimental results demonstrate promising performance, showcasing the potential applicability of our approach in future assembly systems. While further validation with real-world data is warranted, our work contributes to advancing perception systems by proposing a solution for multi-source, multi-object tracking and mapping.

The integration of machine learning and robotics brings promising potential to tackle the application challenges of mobile robot navigation in industries. The real-world environment is highly dynamic and unpredictable, with increasing necessities for efficiency and safety. This demands a multi-faceted approach that combines advanced sensing, robust obstacle detection, and avoidance mechanisms for an effective robot navigation experience. While hybrid methods with default robot operating system (ROS) navigation stack have demonstrated significant results, their performance in real time and highly dynamic environments remains a challenge. These environments are characterized by continuously changing conditions, which can impact the precision of obstacle detection systems and efficient avoidance control decision-making processes. In response to these challenges, this paper presents a novel solution that combines a rapidly exploring random tree (RRT)-integrated ROS navigation stack and a pre-trained YOLOv7 object detection model to enhance the capability of the developed work on the NAV-YOLO system. The proposed approach leveraged the high accuracy of YOLOv7 obstacle detection and the efficient path-planning capabilities of RRT and dynamic windows approach (DWA) to improve the navigation performance of mobile robots in real-world complex and dynamically changing settings. Extensive simulation and real-world robot platform experiments were conducted to evaluate the efficiency of the proposed solution. The result demonstrated a high-level obstacle avoidance capability, ensuring the safety and efficiency of mobile robot navigation operations in aviation environments.

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.

Omni-directional Wheeled Mobile Robots (Omni-WMR) are holonomic system able to move sideways. They have locomotive advantage and are widely deployed in a larger range of application fields, especially in narrowly constrained service-styled manufacturing industries. To ensure smooth motion and full development of locomotive advantages, motion planning draws great attention. Thanks to the mechanism of powered caster wheel (PCW), the motion of Omni-WMR can be decoupled. The collective motion can be decomposed into dimensions of x, y and θ, then planned independently and superposed. In this way trajectory can be planned in the Cartesian space, and later on transformed to the joint space through inverse instantaneous kinematics model for actuation of the motor at each joint. Quintic-polynomial interpolation is used for the case of start to goal with only initial and final configurations known, and piecewise-cubic-spline interpolation is applied for the case of a sequence of intermediate way-points known in the configuration space as input. Here the geometric points for the robot to go through are assumed to be known as a priori without loss of generality. The approaches are implemented in python on ROS (Robot Operating System), and these modular ROS packages are tested on a physical Omni-WMR developed at our lab. Meanwhile, these ROS packages are also tested equivalently in a virtual simulation environment modelled using ROS stage simulator. The experimental results have proved that the motion planning algorithms are able to generate smooth velocity profile for the robot to achieve the goal, which have validated the effectiveness of these algorithms.

An ever-increasing number of robotic applications is being created based on the functionalities and the communication facilities offered by the Robotic Operating System (ROS). When applications involve contact with humans, such as in some transportation tasks, a robot supervisory controller shall guarantee that in every possible circumstance safety and task requirements are satisfied. There is no framework provided by ROS to specify supervisory controllers with formal guarantees about the satisfaction of requirements. This is problematic when the complexity of the controller increases, and it is not viable to test every possible scenario during the validation stage of the robotic application. This paper presents novel work that aims at enabling supervisory controller synthesis for ROS-based applications by (a) proposing a novel mapping between concepts from supervisory control theory and ROS, (b) demonstrating that a supervisory control modeling language exists that can be used to describe plant models and to specify requirements and (c) presenting generic finite-state automata models of ROS components, regarded as plant models, which are input for the synthesis procedure. The approach is demonstrated by synthesizing the controller to coordinate state-of-the-art mobile robot navigation modules. Simulations and real-life experiments show that the generated supervisory controller guarantees the correctness of the model with respect to the requirements and that the execution time of a control loop, including the binding code between the supervisory controller and ROS, is on average short enough to be deployed in high-level control loops. The paper is accompanied by a public code repository that contains the implementation of all methods presented in the paper including simulation environments to ease the replication of the results presented.

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.