Journal articles: 'Mobile robot mapping'

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Tsubouchi, Takashi. "Introduction to Simultaneous Localization and Mapping." Journal of Robotics and Mechatronics 31, no. 3 (June 20, 2019): 367–74. http://dx.doi.org/10.20965/jrm.2019.p0367.

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Simultaneous localization and mapping (SLAM) forms the core of the technology that supports mobile robots. With SLAM, when a robot is moving in an actual environment, real world information is imported to a computer on the robot via a sensor, and robot’s physical location and a map of its surrounding environment of the robot are created. SLAM is a major topic in mobile robot research. Although the information, supported by a mathematical description, is derived from a space in reality, it is formulated based on a probability theory when being handled. Therefore, this concept contributes not only to the research and development concerning mobile robots, but also to the training of mathematics and computer implementation, aimed mainly at position estimation and map creation for the mobile robots. This article focuses on the SLAM technology, including a brief overview of its history, insights from the author, and, finally, introduction of a specific example that the author was involved.

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Ridlwan, Hasvienda Mohammad, Sonki Prasetya, and Musli Min. "2D Mapping Lingkungan Indoor Menggunakan Lidar dan ROS untuk Mobile Robot." Jurnal Mekanik Terapan 3, no. 2 (August 31, 2022): 60–65. http://dx.doi.org/10.32722/jmt.v3i2.4285.

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Currently, the application of control systems has been applied in various scientific fields including mechatronics and robotics. Applications in the branch of robotics are also growing day by day not only with conventional controls but also with intelligent systems. An autonomous robot in carrying out certain missions in an unknown environment requires information about the location itself and the environment through the map. A process to identify a position without a map is called a localization function on the robot. Mobile robots building maps and localization are two fundamental tasks when mobile robots work in indoor environments. With 2D laser scanning (LiDAR) data obtained in real-time, the robot can calculate the area of all empty spaces in a room, then can choose the center of the room as its position for map building. The objective of this research is to implement a two-dimensional mapping method using LiDAR. The algorithm used in this study is the Gmapping Technique on ROS. The main purpose of this research is to map mobile robots with LIDAR sensors using the Robot Operating System for navigation and positioning of mobile robots. Through the actual experimental results, the mobile robot will move with a 2-dimensional mapping process.

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Szeląg, Piotr, Sebastian Dudzik, and Anna Podsiedlik. "Investigation on the Mobile Wheeled Robot in Terms of Energy Consumption, Travelling Time and Path Matching Accuracy." Energies 16, no. 3 (January 22, 2023): 1210. http://dx.doi.org/10.3390/en16031210.

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The task of controlling a wheeled mobile robot is an important element of navigation algorithms. The control algorithm manages the robot’s movement in accordance with the path determined by the planner module, where the accuracy of mapping the given route is very important. Most often, mobile robots are battery-powered, which makes minimizing energy consumption and shortening travel time an important issue. For this reason, in this work, the mobile robot control algorithm was tested in terms of energy consumption, travel time and path mapping accuracy. During the research, a criterion was developed, thanks to which it was possible to select the optimal parameters of the pure pursuit algorithm that controls the movement of the tested robot. The research was carried out in the Laboratory of Intelligent Mobile Robots using the QBot2e mobile robot operating on the basis of differential drive kinematics. As a result of the research, optimal values of the control parameters were obtained, minimizing the travel time, energy consumption and mapping error of the given paths.

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Umetani, Tomohiro, Yuya Kondo, and Takuma Tokuda. "Rapid Development of a Mobile Robot for the Nakanoshima Challenge Using a Robot for Intelligent Environments." Journal of Robotics and Mechatronics 32, no. 6 (December 20, 2020): 1211–18. http://dx.doi.org/10.20965/jrm.2020.p1211.

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Automated mobile platforms are commonly used to provide services for people in an intelligent environment. Data on the physical position of personal electronic devices or mobile robots are important for information services and robotic applications. Therefore, automated mobile robots are required to reconstruct location data in surveillance tasks. This paper describes the development of an autonomous mobile robot to achieve tasks in intelligent environments. In particular, the robot constructed route maps in outdoor environments using laser imaging detection and ranging (LiDAR), and RGB-D sensors via simultaneous localization and mapping. The mobile robot system was developed based on a robot operating system (ROS), reusing existing software. The robot participated in the Nakanoshima Challenge, which is an experimental demonstration test of mobile robots in Osaka, Japan. The results of the experiments and outdoor field tests demonstrate the feasibility of the proposed robot system.

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Deo, Ankit, Ayush Gupta, Himanshu Khemani, and Rashmi Ranjan Das. "Path tracking mobile robot using steppers." E3S Web of Conferences 87 (2019): 01028. http://dx.doi.org/10.1051/e3sconf/20198701028.

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In control of mobile robots, precision plays a key role in path tracking. In this paper we have intended to use hybrid stepper motors for precise control of the two wheeled robot. A control algorithm was developed to control the robot along different trajectories. We have found that stepper motors are more accurate for path tracking than normal DC motors with wheel encoders and one can obtain the implicit coordinates of the robot in runtime more precisely. Getting the precise coordinates of the robot at runtime can be used in various SLAM and VSLAM techniques for more accurate 3D mapping of the environment.

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Sujan, Vivek Anand, Marco Antonio Meggiolaro, and Felipe Augusto Weilemann Belo. "A new technique in mobile robot simultaneous localization and mapping." Sba: Controle & Automação Sociedade Brasileira de Automatica 17, no. 2 (June 2006): 189–204. http://dx.doi.org/10.1590/s0103-17592006000200007.

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In field or indoor environments it is usually not possible to provide service robots with detailed a priori environment and task models. In such environments, robots will need to create a dimensionally accurate geometric model by moving around and scanning the surroundings with their sensors, while minimizing the complexity of the required sensing hardware. In this work, an iterative algorithm is proposed to plan the visual exploration strategy of service robots, enabling them to efficiently build a graph model of their environment without the need of costly sensors. In this algorithm, the information content present in sub-regions of a 2-D panoramic image of the environment is determined from the robot's current location using a single camera fixed on the mobile robot. Using a metric based on Shannon's information theory, the algorithm determines, from the 2-D image, potential locations of nodes from which to further image the environment. Using a feature tracking process, the algorithm helps navigate the robot to each new node, where the imaging process is repeated. A Mellin transform and tracking process is used to guide the robot back to a previous node. This imaging, evaluation, branching and retracing its steps continues until the robot has mapped the environment to a pre-specified level of detail. The effectiveness of this algorithm is verified experimentally through the exploration of an indoor environment by a single mobile robot agent using a limited sensor suite.

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Ganiev, Asilbek, and Kang Hee Lee. "A Study of Autonomous Navigation of a Robot Model based on SLAM, ROS, and Kinect." International Journal of Engineering & Technology 7, no. 3.33 (August 29, 2018): 28. http://dx.doi.org/10.14419/ijet.v7i3.33.18517.

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In this paper, we used a robot operating system (ROS) that is designed to work with mobile robots. ROS provides us with simultaneous localization and mapping of the environment, and here it is used to autonomously navigate a mobile robot simulator between specified points. Also, when the mobile robot automatically navigates between the starting point and the target point, it bypasses obstacles; and if necessary, sets a new path of the route to reach the goal point.

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Nagla, KS, Moin Uddin, and Dilbag Singh. "Dedicated Filter for Robust Occupancy Grid Mapping." IAES International Journal of Robotics and Automation (IJRA) 4, no. 1 (March 1, 2014): 82. http://dx.doi.org/10.11591/ijra.v4i1.pp82-92.

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Sensor based perception of the environment is an emerging area of the mobile robot research where sensors play a pivotal role. For autonomous mobile robots, the fundamental requirement is the convergent of the range information in to high level internal representation. Internal representation in the form of occupancy grid is commonly used in autonomous mobile robots due to its various advantages. There are several sensors such as vision sensor, laser rage finder, and ultrasonic and infrared sensors etc. play roles in mapping. However the sensor information failure, sensor inaccuracies, noise, and slow response are the major causes of an error in the mapping. To improve the reliability of the mobile robot mapping multisensory data fusion is considered as an optimal solution. This paper presents a novel architecture of sensor fusion frame work in which a dedicated filter (DF) is proposed to increase the robustness of the occupancy grid for indoor environment. The technique has been experimentally verified for different indoor test environments. The proposed configuration shows improvement in the occupancy grid with the implementation of dedicated filters.

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Achour, Abdessalem, Hiba Al-Assaad, Yohan Dupuis, and Madeleine El Zaher. "Collaborative Mobile Robotics for Semantic Mapping: A Survey." Applied Sciences 12, no. 20 (October 13, 2022): 10316. http://dx.doi.org/10.3390/app122010316.

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Ensuring safety in human–robot collaboration is one of the main challenges in mobile robotics today. Semantic maps are a potential solution because they provide semantic knowledge in addition to the geometric representation of the environment. They allow robots to perform their basic tasks using geometric representation, mainly localization, path planning and navigation, and additionally allow them to maintain a cognitive interpretation of the environment in order to reason and make decisions based on the context. The goal of this paper is to briefly review semantic mapping for a single mobile robot in indoor environments, and then focus on collaborative mobile semantic mapping. In both contexts, the semantic mapping process is divided into modules/tasks, and recent solutions for each module are discussed. Possible system architectures are also discussed for collaborative semantic mapping. Finally, future directions are highlighted.

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Qin Zhang, and Qingxuan Jia. "Indoor Semantic Mapping for Mobile Robot." International Journal of Digital Content Technology and its Applications 6, no. 22 (December 31, 2012): 648–57. http://dx.doi.org/10.4156/jdcta.vol6.issue22.74.

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Feder, Hans Jacob S., John J. Leonard, and Christopher M. Smith. "Adaptive Mobile Robot Navigation and Mapping." International Journal of Robotics Research 18, no. 7 (July 1999): 650–68. http://dx.doi.org/10.1177/02783649922066484.

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Hähnel, Dirk, Dirk Schulz, and Wolfram Burgard. "Mobile robot mapping in populated environments." Advanced Robotics 17, no. 7 (January 2003): 579–97. http://dx.doi.org/10.1163/156855303769156965.

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13

Peng, Tao, Dingnan Zhang, Don Lahiru Nirmal Hettiarachchi, and John Loomis. "An Evaluation of Embedded GPU Systems for Visual SLAM Algorithms." Electronic Imaging 2020, no. 6 (January 26, 2020): 325–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.6.iriacv-074.

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Simultaneous Localization and Mapping (SLAM) solves the computational problem of estimating the location of a robot and the map of the environment. SLAM is widely used in the area of navigation, odometry, and mobile robot mapping. However, the performance and efficiency of the small industrial mobile robots and unmanned aerial vehicles (UAVs) are highly constrained to the battery capacity. Therefore, a mobile robot, especially a UAV, requires low power consumption while maintaining high performance. This paper demonstrates holistic and quantitative performance evaluations of embedded computing devices that run on the Nvidia Jetson platform. Evaluations are based on the execution of two state-of-the-art Visual SLAM algorithms, ORB-SLAM2 and OpenVSLAM, on Nvidia Jetson Nano, Nvidia Jetson TX2, and Nvidia Jetson Xavier.

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Makarov, M. V., A. V. Astafiev, and I. A. Semenov. "The Study of Intelligent Control Elements of a Mobile Robot and Ensuring Information Security of the Process of Its Functioning in a Dynamic Environment." Proceedings of the Southwest State University 26, no. 2 (February 13, 2023): 72–86. http://dx.doi.org/10.21869/2223-1560-2022-26-2-72-86.

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Purpose of research. The study presented in this article is aimed at establishing and substantiating the principles of effective incorporation of intelligent elements of the control system of a mobile robot operating in a dynamic environment. The subject of the study was the procedure of simultaneous localization and mapping involved in the control. Indicators related to ensuring the information security of the process of robot functioning in real operating conditions were used as the performance criterion.Methods. A methodology for the experimental study of the software procedure execution for simultaneous localization and mapping within the framework of the task of controlling a mobile robot has been developed and implemented. The main focus of the study is a computer model of an abstract mobile robot performing reconnaissance functions in a virtual dynamic environment. The incorporated elements of intelligent information processing into the procedure of simultaneous localization and mapping are convolutional and fully connected neural network layers that provide filtering of dynamic objects.Results. When conducting this experimental study, a simulation of the process of functioning of a computer model of a reconnaissance mobile robot in a virtual environment has been performed. Similar experiments have been reproduced with various structural and functional configurations of the procedure for simultaneous localization and mapping. Quantitative results have been obtained, demonstrating the accuracy of positioning the subject of the study for each of the methods of organizing this procedure. A comparative analysis of the options for using the elements of intelligent information processing within this procedure has been carried out.Conclusion. It has been established that incorporation of the elements of intelligent information processing into the procedure of simultaneous localization and mapping has an impact on the positioning accuracy of a mobile robot and the reliability of its functioning in a dynamic environment. This contributes to the compliance with information security standards when using mobile robots in real operating conditions. It is also determined that there is their excessive use, which leads to a deviation from the optimal qualities necessary for effective autonomous control of a mobile robot and information security provision.

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Habib, Maki K. "Real Time Mapping and Dynamic Navigation for Mobile Robots." International Journal of Advanced Robotic Systems 4, no. 3 (September 1, 2007): 35. http://dx.doi.org/10.5772/5681.

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This paper discusses the importance, the complexity and the challenges of mapping mobile robot's unknown and dynamic environment, besides the role of sensors and the problems inherited in map building. These issues remain largely an open research problems in developing dynamic navigation systems for mobile robots. The paper presenst the state of the art in map building and localization for mobile robots navigating within unknown environment, and then introduces a solution for the complex problem of autonomous map building and maintenance method with focus on developing an incremental grid based mapping technique that is suitable for real-time obstacle detection and avoidance. In this case, the navigation of mobile robots can be treated as a problem of tracking geometric features that occur naturally in the environment of the robot. The robot maps its environment incrementally using the concept of occupancy grids and the fusion of multiple ultrasonic sensory information while wandering in it and stay away from all obstacles. To ensure real-time operation with limited resources, as well as to promote extensibility, the mapping and obstacle avoidance modules are deployed in parallel and distributed framework. Simulation based experiments has been conducted and illustrated to show the validity of the developed mapping and obstacle avoidance approach.

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Wan, Jiuqing, Shaocong Bu, Jinsong Yu, and Liping Zhong. "Distributed simultaneous localization and mapping for mobile robot networks via hybrid dynamic belief propagation." International Journal of Distributed Sensor Networks 13, no. 8 (August 2017): 155014771772671. http://dx.doi.org/10.1177/1550147717726715.

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This article proposes a hybrid dynamic belief propagation for simultaneous localization and mapping in the mobile robot network. The positions of landmarks and the poses of moving robots at each time slot are estimated simultaneously in an online and distributed manner, by fusing the odometry data of each robot and the measurements of robot–robot or robot–landmark relative distance and angle. The joint belief state of all robots and landmarks is encoded by a factor graph and the marginal posterior probability distribution of each variable is inferred by belief propagation. We show how to calculate, broadcast, and update messages between neighboring nodes in the factor graph. Specifically, we combine parametric and nonparametric techniques to tackle the problem arisen from non-Gaussian distributions and nonlinear models. Simulation and experimental results on publicly available dataset show the validity of our algorithm.

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Liu, Xin, Lan Cheng, Yapeng Yang, Gaowei Yan, Xinying Xu, and Zhe Zhang. "An Alpha/Beta Radiation Mapping Method Using Simultaneous Localization and Mapping for Nuclear Power Plants." Machines 10, no. 9 (September 11, 2022): 800. http://dx.doi.org/10.3390/machines10090800.

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Nuclear safety has always been a focal point in the field of nuclear applications. Mobile robots carrying specific sensors for nuclear-radiation detection have become an alternative to manual detection. This work presents an autonomous α/β radiation mapping framework, using a mobile robot carrying a light detection and ranging (LiDAR) and a nuclear-radiation-detection sensor. The method employs simultaneous localization and mapping (SLAM) techniques and radiation-detection sensors. Cartographer is adopted as a demonstration example to map the unknown environment. Radiation data are obtained through the radiation detection sensor and projected onto the environment map after coordinate alignment. The color-coded radiation map is shown on the environment map according to the dose rate. The simulation and real-environment experiments in a robot-operating system (ROS) validate the effectiveness of the proposed method in different radiation scenarios for both indoor and outdoor environments.

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Jia, Lu, Zhengguang Ma, and Yongguo Zhao. "A Mobile Robot Mapping Method Integrating Lidar and Depth Camera." Journal of Physics: Conference Series 2402, no. 1 (December 1, 2022): 012031. http://dx.doi.org/10.1088/1742-6596/2402/1/012031.

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Abstract For mobile robots in uncertain and complex environments, a single sensor obtains a single set of environmental information, creates maps with low accuracy, is easily disturbed by external factors, and easily collides with obstacles in the navigation process. In this paper, we propose a depth camera and LiDAR fusion map building method, which firstly collects data using the LiDAR and depth camera on board the mobile robot, and then preprocesses the collected radar data and depth data and builds a 2D local environment map using RTAB-MAP algorithm. Finally, the local maps are fused using a voxel filter to stitch together a complete global map to complete the map construction. Experimental validation on a ROS-based mobile robot platform shows that the map built using multi-sensor fusion is more complete, presenting more comprehensive environmental information and laying the foundation for subsequent navigation.

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Kimura, Shogo, Hisaya Tanaka, and Hideto Ide. "Automapping by Autonomous Mobile Robot using Ultrasonic Range Sensor." Journal of Robotics and Mechatronics 9, no. 4 (August 20, 1997): 310–16. http://dx.doi.org/10.20965/jrm.1997.p0310.

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For an autonomous mobile robot, how make and describe a environment map is very important problem. I made a robot that using ultrasonic range sensor and autonomous mapping by this robot at wall following algorithm. This experiment shows only using ultrasonic range sensor Mapping problem.

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Rosas-Cervantes, Vinicio Alejandro, Quoc-Dong Hoang, Soon-Geul Lee, and Jae-Hwan Choi. "Multi-Robot 2.5D Localization and Mapping Using a Monte Carlo Algorithm on a Multi-Level Surface." Sensors 21, no. 13 (July 4, 2021): 4588. http://dx.doi.org/10.3390/s21134588.

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Most indoor environments have wheelchair adaptations or ramps, providing an opportunity for mobile robots to navigate sloped areas avoiding steps. These indoor environments with integrated sloped areas are divided into different levels. The multi-level areas represent a challenge for mobile robot navigation due to the sudden change in reference sensors as visual, inertial, or laser scan instruments. Using multiple cooperative robots is advantageous for mapping and localization since they permit rapid exploration of the environment and provide higher redundancy than using a single robot. This study proposes a multi-robot localization using two robots (leader and follower) to perform a fast and robust environment exploration on multi-level areas. The leader robot is equipped with a 3D LIDAR for 2.5D mapping and a Kinect camera for RGB image acquisition. Using 3D LIDAR, the leader robot obtains information for particle localization, with particles sampled from the walls and obstacle tangents. We employ a convolutional neural network on the RGB images for multi-level area detection. Once the leader robot detects a multi-level area, it generates a path and sends a notification to the follower robot to go into the detected location. The follower robot utilizes a 2D LIDAR to explore the boundaries of the even areas and generate a 2D map using an extension of the iterative closest point. The 2D map is utilized as a re-localization resource in case of failure of the leader robot.

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Kawanishi, Ryosuke, Atsushi Yamashita, and Toru Kaneko. "Three-Dimensional Environment Model Construction from an Omnidirectional Image Sequence." Journal of Robotics and Mechatronics 21, no. 5 (October 20, 2009): 574–82. http://dx.doi.org/10.20965/jrm.2009.p0574.

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When mobile robots execute autonomous tasks, map information is important in path planning and self-localization. In unknown environments, mobile robots must generate their own environmental maps. This paper proposes three-dimensional (3D) environment modeling by a mobile robot. The model is generated from results of 3D measurement and texture information. To measure environmental objects efficiently, the robot uses an image sequence acquired by an omnidirectional camera with wide field of view. The measurement method is based on structure from motion. Triangular meshes are constructed from 3D measurement data. The 3D model is constructed by texture mapping to the triangular mesh, proven by experimental result to be effective.

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Karupusamy, Selvaraj, Sundaram Maruthachalam, Suresh Mayilswamy, Shubham Sharma, Jujhar Singh, and Giulio Lorenzini. "Efficient Computation for Localization and Navigation System for a Differential Drive Mobile Robot in Indoor and Outdoor Environments." Revue d'Intelligence Artificielle 35, no. 6 (December 28, 2021): 437–46. http://dx.doi.org/10.18280/ria.350601.

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Numerous challenges are usually faced during the design and development of an autonomous mobile robot. Path planning and navigation are two significant areas in the control of autonomous mobile robots. The computation of odometry plays a major role in developing navigation systems. This research aims to develop an effective method for the computation of odometry using low-cost sensors, in the differential drive mobile robot. The controller acquires the localization of the robot and guides the path to reach the required target position using the calculated odometry and its created new two-dimensional mapping. The proposed method enables the determination of the global position of the robot through odometry calibration within the indoor and outdoor environment using Graphical Simulation software.

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McGarey, Patrick, Kirk MacTavish, François Pomerleau, and Timothy D. Barfoot. "TSLAM: Tethered simultaneous localization and mapping for mobile robots." International Journal of Robotics Research 36, no. 12 (October 2017): 1363–86. http://dx.doi.org/10.1177/0278364917732639.

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Tethered mobile robots are useful for exploration in steep, rugged, and dangerous terrain. A tether can provide a robot with robust communications, power, and mechanical support, but also constrains motion. In cluttered environments, the tether will wrap around a number of intermediate ‘anchor points’, complicating navigation. We show that by measuring the length of tether deployed and the bearing to the most recent anchor point, we can formulate a tethered simultaneous localization and mapping (TSLAM) problem that allows us to estimate the pose of the robot and the positions of the anchor points, using only low-cost, nonvisual sensors. This information is used by the robot to safely return along an outgoing trajectory while avoiding tether entanglement. We are motivated by TSLAM as a building block to aid conventional, camera, and laser-based approaches to simultaneous localization and mapping (SLAM), which tend to fail in dark and or dusty environments. Unlike conventional range-bearing SLAM, the TSLAM problem must account for the fact that the tether-length measurements are a function of the robot’s pose and all the intermediate anchor-point positions. While this fact has implications on the sparsity that can be exploited in our method, we show that a solution to the TSLAM problem can still be found and formulate two approaches: (i) an online particle filter based on FastSLAM and (ii) an efficient, offline batch solution. We demonstrate that either method outperforms odometry alone, both in simulation and in experiments using our TReX (Tethered Robotic eXplorer) mobile robot operating in flat-indoor and steep-outdoor environments. For the indoor experiment, we compare each method using the same dataset with ground truth, showing that batch TSLAM outperforms particle-filter TSLAM in localization and mapping accuracy, owing to superior anchor-point detection, data association, and outlier rejection.

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Weigl, M., B. Siemiȧatkowska, K. A. Sikorski, and A. Borkowski. "Grid-based mapping for autonomous mobile robot." Robotics and Autonomous Systems 11, no. 1 (May 1993): 13–21. http://dx.doi.org/10.1016/0921-8890(93)90004-v.

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Hani, Umme, and Lubna Moin. "Realtime autonomous navigation in V-Rep based static and dynamic environment using EKF-SLAM." IAES International Journal of Robotics and Automation (IJRA) 10, no. 4 (December 1, 2021): 296. http://dx.doi.org/10.11591/ijra.v10i4.pp296-307.

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Localization in an autonomous mobile robot allows it to operate autonomously in an unknown and unpredictable environment with the ability to determine its position and heading. Simultaneous localization and mapping (SLAM) are introduced to solve the problem where no prior information about the environment is available either static or dynamic to achieve standard map-based localization. The primary focus of this research is autonomous mobile robot navigation using the extended Kalman filter (EKF)-SLAM environment modeling technique which provides higher accuracy and reliability in mobile robot localization and mapping results. In this paper, EKF-SLAM performance is verified by simulations performed in a static and dynamic environment designed in V-REP i.e., 3D Robot simulation environment. In this work SLAM problem of two-wheeled differential drive robot i.e., Pioneer 3-DX in indoor static and dynamic environment integrated with Laser range finder i.e., Hokuyo URG-04LX- UG01, LIDAR, and Ultrasonic sensors is solved. EKF-SLAM scripts are developed using MATLAB that is linked to V-REP via remote API feature to evaluate EKF-SLAM performance. The reached results confirm the EKF- SLAM is a reliable approach for real-time autonomous navigation for mobile robots in comparison to other techniques.

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Erdemir, Gokhan, Ahmet Emin Kuzucuoglu, Erkan Kaplanoglu, and Yasser El-Kahlout. "Design and Implementation of Web Based Mobile Robot Control Platform for Robotics Education." Applied Mechanics and Materials 704 (December 2014): 283–87. http://dx.doi.org/10.4028/www.scientific.net/amm.704.283.

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In this paper, we present a particular case of a dynamic, real-time and efficient web-based mobile robot experiment platform (WEB.MREP) design for mobile robotic applications. The design and construction of a multipurpose and WEB.MREP for the application of different path planning and tracking, simultaneous localization and mapping (SLAM) and robot vision techniques for mobile robotic system is the main purpose of this study. The designed and constructed experiment platform consists of five main components: Festo Robotino mobile robot sets, a designed experimental area, a server software, web interfaces (user interfaces) and security measures. The designed platform provides monitoring, real-time controlling and programming of mobile robots for experimental studies and, it helps the users to achieve these studies through a standard web browser without any additional supportive software.

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Andersone, Ilze. "The Characteristics of the Map Merging Methods: A Survey." Scientific Journal of Riga Technical University. Computer Sciences 41, no. 1 (January 1, 2010): 113–21. http://dx.doi.org/10.2478/v10143-010-0032-8.

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The Characteristics of the Map Merging Methods: A SurveyThe development of the autonomous mobile robots is a popular field of research in the artificial intelligence for more than twenty years. An important prerequisite for creation of autonomous robot is the ability to create a map of the environment. However there are several problems in the robotic mapping that are still not completely solved. The use of multiple robots for mapping solves some of these problems, but in this case several new problems, specific to multi-robot mapping, arise. One of the problems in multi-robot mapping is merging all of the local maps that robots have created into one global map. The application of the robot teams in the exploration is a relatively new research field and initially methods for multi-robot mapping were just extended single robot mapping methods. Consequently the map merging problem was simplified. The research of map merging has only recently evolved, thus existing papers on the map merging describe specific map merging methods and there are no comprehensive surveys on the state of the art in the field of map merging. Therefore the goal of this paper is to describe the situation in the field of the map merging and to identify the main characteristics of the map merging methods. The interrelations of these characteristics can then be identified and the corresponding map merging approaches of each characteristic set named.

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Figurowski, Daniel, and Pawel Dworak. "Environment Mapping Algorithm Using Semantic Description and Constrained Delaunay Triangulation." Elektronika ir Elektrotechnika 25, no. 6 (December 6, 2019): 4–7. http://dx.doi.org/10.5755/j01.eie.25.6.24818.

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The article discusses the issue of creating an environment’s map with semantic division of areas. This simplifies some aspects of mobile robot control and permits the robot to carry out tasks issued in a human-understandable form. The article presents an innovative algorithm for mapping the environment by a mobile robot. Its novelty stems from the use of a semantic description of the world and Delaunay triangulation method with constraints. This description is used to segment the map of the environment and to reduce redundant information. The developed algorithm is based on the idea of extending the borders of the already discovered areas; they are expanded as the new data are collected from the environment. These data contain information about the location of semantic types in the explored space and is used to update the areas during analysis of the environment’s map. The beforementioned triangulation method is used in that process. The performance of the proposed algorithm is tested in simulation studies. The obtained results show a good computational efficiency of the method, which is crucial in the problem of environment exploration by mobile robots with limited computational resources.

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Dai, Yanyan, and Suk Gyu Lee. "Multiple Internet of Robotic Things robots based on LiDAR and camera sensors." International Journal of Advanced Robotic Systems 17, no. 2 (March 1, 2020): 172988142091376. http://dx.doi.org/10.1177/1729881420913769.

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A combination of Internet of Things and multiple robots with sensors has been an attractive research topic over the past years. This article proposes an Internet of Robotic Things system structure to monitor events, fuse sensor data, use local robots to determine a best action, and then act to control multiple mobile robots. The Internet of Robotic Things system includes two main layers: the host controller layer and the multiple robots layer. The controller layer communicates with the multiple robots layer by Wi-Fi module. The Internet of Robotic Things system helps finish five tasks: localizing robots, planning paths, avoiding obstacles, moving to waypoint stable, and creating a map. Based on depth data from depth camera and robot posture, a mapping algorithm is proposed to create map. Based on light detection and ranging sensor data and google cartographer, simultaneously localization and mapping (SLAM) is also processed in this article. The fuzzy sliding mode tracking control method is proposed for each robot to guarantee the robot stable moves. Simulation results show the effectiveness of the proposed algorithm and are used to compare with the experiment result. In the experiment, one host computer and two Kobuki mobile robots with light detection and ranging and depth camera sensors are integrated as an Internet of Robotic Things system. Two robots successfully localize themselves and avoid obstacles. The follower robot simultaneously builds a map.

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Fikri, Achmad Akmal, and Lilik Anifah. "Mapping And Localization System Pada Mobile Robot Menggunakan Metode SLAM Berbasis LiDAR." Journal of Information Engineering and Educational Technology 5, no. 1 (June 16, 2021): 27–33. http://dx.doi.org/10.26740/jieet.v5n1.p27-33.

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Permasalahan utama dari autonomous robot atau robot otonom untuk bernavigasi adalah bagaimana robot dapat mengenali lingkungan sekitar. Oleh karena itu, penelitian ini berfokus pada perancangan sistem mapping dan lokalisasi menggunakan metode Simultaneous Localization And Mapping (SLAM) yang diimplementasikan pada mobile robot jenis omnidirectional atau holonomic menggunakan sensor LiDAR. Penelitian ini mengusulkan sistem mapping dan lokalisasi untuk mengenali lingkungan sekitar dengan membuat peta lingkungan menggunakan algoritma google cartographer yang dikombinasikan dengan metode eulerdometry yaitu kombinasi antara odometry dengan data euler orientation dari sensor IMU. Pengujian dilakukan dengan menguji setiap sensor seperti IMU dan LiDAR, dan menguji instegrasi sistem termasuk metode eulerdometry dan sistem mapping dan lokalisasi dengan algoritma google cartographer yang dikombinasikan dengan metode eulerdometry. Hasil pengujian dari sistem mapping dan lokalisasi menunjukkan hasil yang optimal dan mampu mengenali kondisi lingkungan sekitar robot meskipun masih terdapat noise pada peta yang sudah dibuat.

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Kasoep, Werman. "RANCANG BANGUN MOBILE ROBOT MICROMOUSE UNTUK PENCARIAN RUTE TERPENDEK MENGGUNAKAN ALGORITMA FLOOD FILL." Journal on Information Technology and Computer Engineering 1, no. 01 (March 20, 2017): 8–16. http://dx.doi.org/10.25077/jitce.1.01.8-16.2017.

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Mobile robot merupakan salah satu jenis robot yang banyak digunakan dalam membantu pekerjaan manusia. Mobile robot micromouse adalah salah satu jenis robot yang dibuat agar dapat mencari rute terpendek dalam sebuah labirin atau maze. Algoritma flood fill merupakan salah satu cara untuk mencari rute terpendek dengan melakukan maze mapping, dimana robot akan membandingkan nilai dari setiap cell dan bergerak menuju cell terkecil. Dalam penelitian yang dilakukan, mobile robot micromouse dirancang menggunakan Sensor Ultrasonic PING untuk mendeteksi halangan dan Arduino Uno yang akan mengontrol pergerakan dari motor DC. Robot akan mencari rute terpendek berdasarkan maze mapping yang telah dilaluinya. Robot diujikan pada 3 buah labirin atau maze, dimana setiap labirin atau maze dilakukan 10 kali pengujian dan diperoleh keberhasilan sebesar 80% pada maze 1, pada maze 2 sebesar 70% dan pada maze 3 sebesar 70%.

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Rasam, Himanshu R., Tadeusz Mikolajczyk, and Danil Yurievich Pimenov. "The State of the Art of Mobile Robots on a Solid Surface." Applied Mechanics and Materials 783 (August 2015): 57–68. http://dx.doi.org/10.4028/www.scientific.net/amm.783.57.

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This paper presents an overview of the state of art of mobile robots presenting aspects of their current level of sophistication, fields mobile robots are being presently used, the future application and scope of mobile robots. Classifying the primary research topics of multi-robot systems into seven major categories (Biological inspirations, architecture, localization/mapping/exploration, object transport and manipulation, motion co-ordination, reconfigurable and learning robot systems, vision), we discuss the current state of research in these areas. An attempt to cover the points for which a general agreement has emerged within the scientific domain, are then presented, followed by a discussion of alternative approaches and currently unsolved problems. This paper also reviews the most relevant literature and previous research activity regarding mobile robotics. We conclude by identifying more topics of scientific consensus, open research issues and a discussion of some application areas where mobile robots could be used with existing technology.

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Mori, Toshihiro. "2-D distance image sensor." Journal of Robotics and Mechatronics 15, no. 3 (June 20, 2003): 356–57. http://dx.doi.org/10.20965/jrm.2003.p0356.

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The PBS series microscopic distance image sensor was developed for automatic guided vehicles (AGVs) and autonomously mobile intelligent robots. AGV is used with a PBS sensor for detecting obstacles forward when the AGV is moving. The PBS outputs angle and distance data horizontally. The autonomously mobile intelligent robot uses the PBS for environmental mapping.

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Cai-Wen Niu, Cai-Wen Niu, Shi-Hui Dong Cai-Wen Niu, and Yan Zeng Shi-Hui Dong. "Research on Improved Algorithm of Mobile Robot Vision SLAM Based on Depth Camera." 電腦學刊 33, no. 5 (October 2022): 197–211. http://dx.doi.org/10.53106/199115992022103305017.

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Aiming at the problems of low accuracy, poor real-time performance and large cumulative error in traditional depth camera visual synchronization positioning and map construction algorithms, this paper proposes an improved visual SLAM algorithm for mobile robots based on depth camera. According to the realization process of robot vision, a feature point extraction algorithm based on feature extraction and image segmentation watershed algorithm is proposed in the front-end process, which improves the real-time performance of the algorithm. Then the RE-RANSAC algorithm is used to eliminate the mismatched feature points to improve the matching accuracy, and then the accumulated error is eliminated through closed loop detection, and finally the process of robot mapping is completed. After simulation experiments, the feasibility of the improved algorithm is proved, and the robot’s mapping and trajectory estimation are completed.

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Li, Bin, Zhengguang Ma, and Yongguo Zhao. "2D Mapping of Mobile Robot Based on micro-ROS." Journal of Physics: Conference Series 2402, no. 1 (December 1, 2022): 012030. http://dx.doi.org/10.1088/1742-6596/2402/1/012030.

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Abstract This paper proposes micro-ROS-based mobile robot system development and 2D mapping. Micro-ROS uses the micro-XRCE-DDS middleware to communicate with ROS2 in an extremely resource-constrained microcontroller environment. The mobile robot control system adopts a layered control method. The x64 PC equipped with Ubuntu ROS foxy is used as the main control platform, the olimex-stm32-E407 equipped with micro-ROS is used as the motion control layer, and the stm32F103 microcontroller is the underlying driver core. The forward and inverse kinematic equations are obtained through the analysis of the robot kinematics model. Through the interaction between the ROS2 nodes in the micro-ROS environment and the ROS2 nodes on the PC side, the cartographer algorithm is used to verify the effectiveness of the micro-ROS-based mobile robot system.

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Zhang, Wen Zhao, Zhi Zhuang Liu, and Wen Zhen Zhou. "Unknown Environment Mapping Based on Multiple Micro-Robots." Applied Mechanics and Materials 556-562 (May 2014): 2496–501. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2496.

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Currently, most mapping researches are deeply dependent on long range sensors such as sonar, laser range finders or cameras, but due to power or cost constraints, which often cannot be mounted in the sensor module. In response to the no long range sensors situation, this paper presents a method to complete indoor mapping tasks in real-time only using short range sensors installed on the multiple micro-robots. The key mapping problem needed to address in the paper is that each single robot created a map based on odometer and short distance sensor data, and then maps from multiple robots were consolidated to generate a greater coherence map. A kind of SRV mobile robots were tested in an artificial rectangular environment. The experimental results show that the multiple micro-robot system has good robustness to odometer and environmental noise.

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Rone, William, and Pinhas Ben-Tzvi. "Mapping, localization and motion planning in mobile multi-robotic systems." Robotica 31, no. 1 (February 9, 2012): 1–23. http://dx.doi.org/10.1017/s0263574712000021.

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SUMMARYAs researchers have pushed the limits of what can be accomplished by a single robot operating in a known or unknown environment, a greater emphasis has been placed on the utilization of mobile multi-robotic systems to accomplish various objectives. In transitioning from a robot-centric approach to a system-centric approach, considerations must be made for the computational and communicative aspects of the group as a whole, in addition to electromechanical considerations of individual robots. This paper reviews the state-of-the-art of mobile multi-robotic system research, with an emphasis on the confluence of mapping, localization and motion control of robotic system. Methods that compose these three topics are presented, including areas of overlap, such as integrated exploration and simultaneous localization and mapping. From these methods, an analysis of benefits, challenges and tradeoffs associated with multi-robotic system design and use are presented. Finally, specific applications of multi-robotic systems are also addressed in various contexts.

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Wang, Jun, and Yasutake Takahashi. "Particle Smoother-Based Landmark Mapping for the SLAM Method of an Indoor Mobile Robot with a Non-Gaussian Detection Model." Journal of Sensors 2019 (October 14, 2019): 1–19. http://dx.doi.org/10.1155/2019/3717298.

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HF-band radio-frequency identification (RFID) is a robust identification system that is rarely influenced by objects in the robot activity area or by illumination conditions. An HF-band RFID system is capable of facilitating a reasonably accurate and robust self-localization of indoor mobile robots. An RFID-based self-localization system for an indoor mobile robot requires prior knowledge of the map which contains the ID information and positions of the RFID tags used in the environment. Generally, the map of RFID tags is manually built. To reduce labor costs, the simultaneous localization and mapping (SLAM) technique is designed to localize the mobile robot and build a map of the RFID tags simultaneously. In this study, multiple HF-band RFID readers are installed on the bottom of an omnidirectional mobile robot and RFID tags are spread on the floor. Because the tag detection process of the HF-band RFID system does not follow a standard Gaussian distribution, extended Kalman filter- (EKF-) based landmark updates are unsuitable. This paper proposes a novel SLAM method for the indoor mobile robot with a non-Gaussian detection model, by using the particle smoother for the landmark mapping and particle filter for the self-localization of the mobile robot. The proposed SLAM method is evaluated through experiments with the HF-band RFID system which has the non-Gaussian detection model. Furthermore, the proposed SLAM method is also evaluated by a range and bearing sensor which has the standard Gaussian detection model. In particular, the proposed method is compared against two other SLAM methods: FastSLAM and SLAM methods utilize particle filter for both the landmark updating and robot self-localization. The experimental results show the validity and superiority of the proposed SLAM method.

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Han, Xiaoning, Shuailong Li, Xiaohui Wang, and Weijia Zhou. "Semantic Mapping for Mobile Robots in Indoor Scenes: A Survey." Information 12, no. 2 (February 22, 2021): 92. http://dx.doi.org/10.3390/info12020092.

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Sensing and mapping its surroundings is an essential requirement for a mobile robot. Geometric maps endow robots with the capacity of basic tasks, e.g., navigation. To co-exist with human beings in indoor scenes, the need to attach semantic information to a geometric map, which is called a semantic map, has been realized in the last two decades. A semantic map can help robots to behave in human rules, plan and perform advanced tasks, and communicate with humans on the conceptual level. This survey reviews methods about semantic mapping in indoor scenes. To begin with, we answered the question, what is a semantic map for mobile robots, by its definitions. After that, we reviewed works about each of the three modules of semantic mapping, i.e., spatial mapping, acquisition of semantic information, and map representation, respectively. Finally, though great progress has been made, there is a long way to implement semantic maps in advanced tasks for robots, thus challenges and potential future directions are discussed before a conclusion at last.

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Evita, Maria. "A Review of Mobile Robot Navigation System for Volcano Monitoring Application." Indonesian Journal of Physics 32, no. 1 (November 5, 2021): 1–11. http://dx.doi.org/10.5614/ijp.v32i1.300.

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Volcano is a geological environment including magma, eruption, volcanic edifice and its basements. For continuous monitoring after eruption, a mobile robot could be proposed as an alternative to prevent hazardous effect to volcanologist who perform up close monitoring. In this paper, the robots were divided into 3 types according to their different structures: legged, track-legged and wheeled mobile robots. Meanwhile, the navigation system were implemented in 4 steps suitable for volcano condition: environment mapping, trajectory design, motion control and obstacle avoidance. These navigation system also tested in different locations: indoor, outdoor and real volcano with different testing method for these robots. The testing result was discussed in robot kinematics parameter such as trajectory, velocity, slope angle, rollover and sideslip angels.

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Evita, Maria. "A Review of Mobile Robot Navigation System for Volcano Monitoring Application." Indonesian Journal of Physics 32, no. 1 (November 5, 2021): 1–11. http://dx.doi.org/10.5614/itb.ijp.2021.32.1.1.

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Volcano is a geological environment including magma, eruption, volcanic edifice and its basements. For continuous monitoring after eruption, a mobile robot could be proposed as an alternative to prevent hazardous effect to volcanologist who perform up close monitoring. In this paper, the robots were divided into 3 types according to their different structures: legged, track-legged and wheeled mobile robots. Meanwhile, the navigation system were implemented in 4 steps suitable for volcano condition: environment mapping, trajectory design, motion control and obstacle avoidance. These navigation system also tested in different locations: indoor, outdoor and real volcano with different testing method for these robots. The testing result was discussed in robot kinematics parameter such as trajectory, velocity, slope angle, rollover and sideslip angels.

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Jun, Lin, Zi Bin, and Wu Xia. "Research on Cooperation Localization and Mapping for Hoisting Multi-Robot System." Applied Mechanics and Materials 229-231 (November 2012): 2248–52. http://dx.doi.org/10.4028/www.scientific.net/amm.229-231.2248.

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According to the practical operation of the hoisting multi-mobile robots system (HMRS), the cooperation localization and mapping is studied. Firstly, an improved algorithm of cooperation localization solution of multiple robot system is proposed based on map gridding algorithm. In addition, by virtue of sensor technology, the grid method is designed, which has the ability of accurate, reliable localization and rapid local mapping of the HMRS. Finally, simulation results demonstrate that the localization and mapping system is feasible and efficient.

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Tasaki, Tsuyoshi, Fumio Ozaki, Nobuto Matsuhira, Tetsuya Ogata, and Hiroshi G. Okuno. "People Detection Based on Spatial Mapping of Friendliness and Floor Boundary Points for a Mobile Navigation Robot." Journal of Robotics 2011 (2011): 1–10. http://dx.doi.org/10.1155/2011/683975.

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Navigation robots must single out partners requiring navigation and move in the cluttered environment where people walk around. Developing such robots requires two different people detections: detecting partners and detecting all moving people around the robots. For detecting partners, we design divided spaces based on the spatial relationships and sensing ranges. Mapping the friendliness of each divided space based on the stimulus from the multiple sensors to detect people calling robots positively, robots detect partners on the highest friendliness space. For detecting moving people, we regard objects’ floor boundary points in an omnidirectional image as obstacles. We classify obstacles as moving people by comparing movement of each point with robot movement using odometry data, dynamically changing thresholds to detect. Our robot detected 95.0% of partners while it stands by and interacts with people and detected 85.0% of moving people while robot moves, which was four times higher than previous methods did.

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Aslan, Gökhan, Erhan Ilhan Konukseven, and Buğra Koku. "Design and Implementation of a 3D Range Scanner for Mobile Robots." Key Engineering Materials 572 (September 2013): 644–47. http://dx.doi.org/10.4028/www.scientific.net/kem.572.644.

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In an efficient autonomous navigation and exploration, the robots should sense the environment as exactly as possible in real-time and act correctly on the basis of the acquired 3D data. Laser scanners have been used for the last 30 years for mobile robot navigation. However, they often did not enough speed, accuracy and field of view. In this paper we present the design and implementation of a scanning platform, which can be used for both outdoor and indoor mobile robot navigation and mapping. A 3D scanning platform based on a 2D laser rangefinder was designed in compact way for fast and accurate mapping with maximum field of view. The range finder is rotated around the vertical axis to extract the 3D indoor information. However, the scanner is designed to be placed in any direction on a mobile robot. The designed mechanism provides 360º degree horizontal by 240º degree vertical field of view. The maximum resolution is 0.36º degrees in elevation and variable in azimuth (0.1 degrees if scanning platform is set to complete a 360º degree rotation in 3.6 seconds). The proposed low cost compact design is tested by scanning a physical environment with known dimensions to show that it can be used as a precise and reliable high quality 3D sensor for autonomous mobile robots.

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Kühnlenz, Kolja, and Martin Buss. "Multi-Focal Vision and Gaze Control Improve Navigation Performance." International Journal of Advanced Robotic Systems 9, no. 1 (January 1, 2012): 25. http://dx.doi.org/10.5772/50920.

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Multi-focal vision systems comprise cameras with various fields of view and measurement accuracies. This article presents a multi-focal approach to localization and mapping of mobile robots with active vision. An implementation of the novel concept is done considering a humanoid robot navigation scenario where the robot is visually guided through a structured environment with several landmarks. Various embodiments of multi-focal vision systems are investigated and the impact on navigation performance is evaluated in comparison to a conventional mono-focal stereo set-up. The comparative studies clearly show the benefits of multi-focal vision for mobile robot navigation: flexibility to assign the different available sensors optimally in each situation, enhancement of the visible field, higher localization accuracy, and, thus, better task performance, i.e. path following behavior of the mobile robot. It is shown that multi-focal vision may strongly improve navigation performance.

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Trujillo, Juan-Carlos, Rodrigo Munguia, and Antoni Grau. "Aerial Cooperative SLAM for Ground Mobile Robot Path Planning." Engineering Proceedings 6, no. 1 (May 20, 2021): 65. http://dx.doi.org/10.3390/i3s2021dresden-10164.

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The trajectory planning for ground mobile robots operating in unknown environments can be a difficult task. In many cases, the sensors used for detecting obstacles only provide information about the immediate surroundings, making it difficult to generate an efficient long-term path. For instance, a robot can easily choose to move along a free path that, eventually, will have a dead end. This research is intended to develop a cooperative scheme of visual-based aerial simultaneous localization and mapping (SLAM) that will be used for generating a safe long-term trajectory for a ground mobile robot. The general idea is to take advantage of the high-altitude point of view of aerial robots to obtain spatial information of a wide area of the surroundings of the robot. In this case, it could be seen as having a zenithal picture of the labyrinth to solve the robot’s path. More specifically, the system will generate a wide area spatial map of the ground robot’s obstacles from the images taken by a team of aerial robots equipped with onboard cameras, by means of a cooperative visual-based SLAM method. At the same time, the map will be used to generate a safe path for the ground mobile robot. While the ground robot moves, its onboard sensors will be used to refinine the map and, thus, to avoid obstacles that were not detected from the aerial images.

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Paull, Liam, Mae Seto, John J. Leonard, and Howard Li. "Probabilistic cooperative mobile robot area coverage and its application to autonomous seabed mapping." International Journal of Robotics Research 37, no. 1 (November 30, 2017): 21–45. http://dx.doi.org/10.1177/0278364917741969.

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There are many applications that require mobile robots to autonomously cover an entire area with a sensor or end effector. The vast majority of the literature on this subject is focused on addressing path planning for area coverage under the assumption that the robot’s pose is known or that error is bounded. In this work, we remove this assumption and develop a completely probabilistic representation of coverage. We show that coverage is guaranteed as long as the robot pose estimates are consistent, a much milder assumption than zero or bounded error. After formally connecting robot sensor uncertainty with area coverage, we propose an adaptive sliding window filter pose estimator that provides a close approximation to the full maximum a posteriori estimate with a computation cost that is bounded over time. Subsequently, an adaptive planning strategy is presented that automatically exploits conditions of low vehicle uncertainty to more efficiently cover an area. We further extend this approach to the multi-robot case where robots can communicate through a (possibly faulty and low-bandwidth) channel and make relative measurements of one another. In this case, area coverage is achieved more quickly since the uncertainty over the robots’ trajectories is reduced. We apply the framework to the scenario of mapping an area of seabed with an autonomous underwater vehicle. Experimental results support the claim that our method achieves guaranteed complete coverage notwithstanding poor navigational sensors and that resulting path lengths required to cover the entire area are shortest using the proposed cooperative and adaptive approach.

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Rahman, Mohammad Ikmal Shamin Abdul. "Exploring Mapping and Avoidance Simulation for Mobile Robot." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 1.3 (June 25, 2020): 381–87. http://dx.doi.org/10.30534/ijatcse/2020/6091.32020.

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Fiorini, Federico Bertolli Paolo. "VISUAL SLAM — MOBILE ROBOT LOCALIZATION WITH ENVIRONMENT MAPPING." IFAC Proceedings Volumes 39, no. 15 (2006): 286–91. http://dx.doi.org/10.3182/20060906-3-it-2910.00049.

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Abd Rahman, Nur Aira, Khairul Salleh Mohamed Sahari, Muhammad Fairuz Abdul Jalal, Anwar Abdul Rahman, Mohd Izwan Abd Adziz, and Mohd Zaid Hassan. "Mobile robot for radiation mapping in indoor environment." IOP Conference Series: Materials Science and Engineering 785 (May 27, 2020): 012021. http://dx.doi.org/10.1088/1757-899x/785/1/012021.

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Journal articles on the topic 'Mobile robot mapping'

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