A topological navigation system for indoor environments based on perception events

Accurate and reliable localization is a base requirement for a mobile robot. Landmark localization is a very common technique to do this. The arrangement of landmarks is important for reliable and accurate estimation of localization data. In this paper the optimized arrangement of artificial landmarks is discussed and a solution for building an accurate and reliable localization system based on combining artificial and natural landmarks is presented. Landmark placement is done by definition and optimization of a confidence level describing the expected localization estimation error. Areas in the environment which are ill conditioned by natural landmarks are furnished with additional artificial landmarks. The proposed system has been tested in a simulation and with a mobile robot in a real office environment.

Generally, the mobile robots are researched and used in many applications. The fundamental method of navigation is localization, which includes aspects such as sensing, mapping, localization, planning and control. In order to we build the navigation system for mobile robot operating in unmodified environments, we must know the mobile robot’s position in reference frame. Typically reference frame is a Cartesian or we can specify the landmark as reference position. The dead reckoning based on odometry is widely to identify and classify landmarks. Dead reckoning systems estimate the position of the mobile robot with respect to an initial point by measuring linear and angular velocities or accelerations. There are also hybrid models, which have sensor systems of both dead reckoning and landmark recognition. Dead reckoning is the estimate of current position based on a previously determined position or estimated speeds from a known past position over time. This can also determine the future position by projecting an ordered course and speed of advance from a known present position. A disadvantage of dead reckoning is that new positions are calculated solely from previous positions. The errors of the process are cumulative, so the error in the position fix grows with time (Hans-Joachim von der Hardt & Didier Wolf Rene Husson, 1996; Hye Ri Park et al., 2009). Odometry is widely used in mobile robots to estimate their position relative to reference frame. This method estimates the position over time with errors accumulation due to the integration of velocity measurements (Cristina Tar in Sauer and Hermann Brugger, 2001; Houshangi, N. and Azizi, F., 2006; Agostino Martinelli, 2002). The navigation system for mobile robot based on odometry has typically problem with inaccuracy. Because of the odometry problems navigation systems for mobile robot application cannot work solely on dead reckoning. This is the reason that the navigation system needs the addition of sensory information. Additional sensors can be defined as information provided to assist the accomplishment of a task by a system. Practically, sensor fusion refers to the integration process where the different sources of sensory information are combined into one representational format. Typically in navigation systems sensory information can be fused from complementary sensors, redundant sensors or even from a single sensor over a period of time. The advantage of sensory fusion provides uncertainty and noise reduction, failure toleration and extended flexibility of sensor ability.

In this paper, we present an omnidirectional artificial landmark model and a robust artificial landmark recognition algorithm for indoor mobile robot positioning. The landmark model encodes identities with nested circles in black and white, which provides stable edge response and enables strong tolerance to various lighting conditions and perspective distortions. The corresponding positioning system uses a single upward-facing webcam as the vision sensor to capture landmarks. To address the effect of the lighting and sensing noise, the topological contour analysis is applied to detect landmarks, and the dynamic illumination adjustment is used to assist landmark recognition. Based on the landmark recognition, the absolute position of the camera in the environment is estimated using a trilateration algorithm. The landmark model and positioning system are tested with a mobile robot in a real indoor environment. The results show that the purposed technique provides autonomous indoor positioning for mobile robots with high robustness and consistency.

Owing to the presence of multiple obstacles or changes in the positions of surroundings objects, the localization of a mobile robot using only natural landmarks is difficult in indoor environments. Consequently, in these environments, the use of artificial landmarks to achieve robust localization is necessary. To this end, we propose a global localization method for mobile robots, using reflectors as indistinguishable artificial landmarks, not the manmade signs have a certain pattern. In previous studies, it has been shown that localization is possible using information about the distance between indistinguishable artificial landmarks. We applied this principle in a real-world situation with many obstacles or in a dynamic environment and confirmed that robust, high-accuracy global localization is possible.

One of the major successes in ITS technology lately has been in the area of Advanced Traveler Information Systems (ATIS). In particular, there are now many good navigation systems that can advise drivers on route selection within a roadway network. This type of navigation software is now available on the Internet that can be used for trip planning, as well as in on-board navigation systems that can calculate optimal routes based on current vehicle location. The primary role of these navigation systems is to help drivers find their destinations. More advanced navigation systems can now help drivers avoid congestion on the roadways based on receiving real-time roadway data information. To date, these navigation systems have embedded algorithms that attempt to minimize trip distance and/or travel time. However, many drivers are now becoming increasingly concerned with rising fuel costs and pollutant emissions. Therefore, it is desirable to create new "environmentally-friendly" navigation algorithms. Taking advantage of the latest navigation technology, we have developed new navigation techniques that focus on minimizing energy consumption and pollutant emissions. These methods combine sophisticated mobile-source energy and emission models with route minimization algorithms that are used for navigational purposes. These new methods have been applied in several Southern California case studies resulting in significant energy savings compared to standard navigation techniques.

Mobile robot, especially wheeled mobile robot, with its simple mechanical structure and inherent agility, has attracted significant attentions for dynamic environment applications in the last two decades (Pin et al. 1994 and Purwin 2006). A general mobile robot Guidance, Navigation and Control (GNC) system is illustrated in the following figure. The main function of guidance system is to generate a feasible trajectory command, usually in multiple dimension of freedom (DOF) to achieve a robot task. The objective of control system is to drive the mobile robot following the commanded trajectory with acceptable tracking errors and stability margin. The function of robot navigation system is to provide accurate position, velocity and/or orientation information for the control system and guidance system. A stable and accurate navigation system is the bridge between the guidance system and control system of mobile robots, which ultimately determines the robot performance.

We describe an integrated navigation system based on Global Navigation Satellite Systems (GNSS), an Inertial Navigation System (INS) and terrestrial ranging technologies that can support accurate and seamless indoor-outdoor positioning. To overcome severe multipath disturbance in indoor environments, Locata technology is used in this navigation system. Such a “Locata-augmented” navigation system can operate in different positioning modes in both indoor and outdoor environments. In environments where GNSS is unavailable, e.g. indoors, the proposed system is designed to operate in the Locata/INS “loosely-integrated” mode. On the other hand, in outdoor environments, all GNSS, Locata and INS measurements are available, and all useful information can be fused via a decentralised Federated Kalman filter. To evaluate the proposed system for seamless indoor-outdoor positioning, an indoor-outdoor test was conducted at a metal-clad warehouse. The test results confirmed that the proposed navigation system can provide continuous and reliable position and attitude solutions, with the positioning accuracy being better than five centimetres.

Localization of a vehicle is a key component for driving assistance or autonomous navigation. In this work, we propose a visual positioning system (VPS) for vehicle or mobile robot navigation. Different from general landmark-based or model-based approaches, which rely on some predefined known landmarks or a priori information about the environment, no assumptions on the prior knowledge of the scene are made. A stereo-based vision system is built for both extracting feature correspondences and recovering 3-D information of the scene from image sequences. Relative positions of the camera motion are then estimated by registering the 3-D feature points from two consecutive image frames. Localization of the vehicle is finally given by the reference to its initial position. I. INTRODUCTION Localization and tracking of a vehicle are major compo- nents for providing positions, directions and travel informa- tion to the driver. They also serve as key technologies for building autonomous navigation systems. Many approaches have been proposed for locating a mobile robot or a ve- hicle based on various techniques. The most commonly used methods include dead-reckoning techniques, navigation using active beacons, landmark-based navigation, map-based navigation, global positioning system (GPS), and vision- based positioning. Dead-reckoning is a procedure for determining the present location of a vehicle by advancing some previous position through known path and velocity information over a given period of time. Since an odometer and optical sensors for wheel direction detection are easily installed on a vehicle, dead-reckoning is usually a less expensive method for vehicle localization. However, the integration of incremental motion information over time will lead to accumulation of errors. In the case of long distance travel, accumulation of orientation error will make position errors diverge as the increase of driving time. Active beacons such as laser, sonar or radio can be used as media for vehicle or mobile robot navigation. These approaches use triangulation to measure the distance between a number of beacons and the mobile platform and then deter- mine the current location. The problems associated with this technique are the inaccuracy of the distance measurement caused by time-delay of the signals, and the installation and maintenance cost of a large number of beacons required for an area. Satellite based differential global positioning system (DGPS) is probably the most advanced and accurate method for identifying the position and orientation of an object. However, DGPS does not work well if the satellite signals are blocked. This situation commonly happens in the indoor environments, or urban areas with tall buildings, etc. In this work, we propose a visual positioning system (VPS) for vehicle or mobile robot navigation. Different from general landmark-based or model-based approaches which rely on some predefined known landmarks or ap rioriinfor- mation about the environment (e.g., 3-D models of buildings, objects, etc.), no assumptions on the prior knowledge of the scene are made. A stereo-based vision system is built for both extracting feature correspondences and recovering 3- D information of the scene from image sequences. A robust feature tracking method based on simultaneously considering the inter-frame images from the same camera and the stereo image pair from different cameras at a fixed time instant is developed. The relative position of the camera motion is then estimated by registering the 3-D feature points from two consecutive image frames. Our system includes the following basic modules:

The Air Force Research Laboratory Munitions Directorate (AFRL/MN) is engaged in the development of advanced munition navigation systems. The Navigation Team has a mobile test vehicle (MTV) to accomplish outdoor, low-dynamic testing of developmental navigation systems. Past tests conducted by the MTV required the units under test possess their own navigation system while the MTV provided F-16 data interface emulation via MIL-STD-1553 data bus. A reconfigurable navigation system (RNS) is being developed that accepts data from any inertial measurement unit (IMU), such as micro electromechanical systems (MEMS), and provides the means for navigation evaluation. Implementation of navigation filters will be done with the aid of a user interface while tests of these filters will be done without paying the price of airborne tests. C++ objects for such things as the plant, measurement, and covariance matrices, states, the earth, position, velocity, and transformation matrices are created. This paper discusses the beginning stages of the RNS. It begins with the verification of a strap-down navigator. The timing of the inertial navigation system is explained. Position and velocity accuracy is provided.

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.

In recent years, due to the declining birthrate, there are more and more situations where elderly people need to be cared for. As a result, the burden per caregiver has been increasing. As a technology to solve this problem, indoor robot navigation systems that support indoor mobility of those who need care are attracting attention. A navigation system using a mobile robot has components such as position estimation and movement control. As a conventional technique for location estimation, there is a location estimation method using QR codes. In addition, there is a conventional method of mobile control using RSSI. The proposed method presented in this paper is a robot navigation system that replaces QR codes with RFID tags and uses the RSSI-based mobile control method. In addition, we propose a path recovery system when the mobile robot deviates from the path during navigation. In the performance evaluation of navigation between arbitrary points, the maximum error from the ideal trajectory was found to be 13 cm. We also found that the average error between the destination tag and the stop position was 20 cm, while the maximum error was 51 cm. In the performance evaluation of the path recovery system, the maximum error between the ideal path and the stop position was found to be 30 cm.

Autonomous mobile robotic teams are increasingly used in exploration of indoor environments. Accurate modeling of the world around the robot and describing the interaction of the robot with the world greatly increases the ability of the robot to act autonomously. This paper demonstrates the ability of autonomous robotic teams to find objects of interest. A novel feature of our approach is the object discovery and the use of it to augment the mapping and navigation process. The generated map can then be decomposed into semantic regions while also considering the distance and line of sight to anchor points. The advantage of this approach is that the robot can return a dense map of the region around an object of interest. The robustness of this approach is demonstrated in indoor environments with multiple platforms with the objective of discovering objects of interest.

Introduction: Autonomous mobile robots (AMRs) represent a transformative technology revolutionizing various industries, from manufacturing and logistics to healthcare and service sectors. These robots, equipped with advanced sensors, artificial intelligence (AI), and navigation systems, can operate independently in dynamic environments without human intervention. The emergence of AMRs has significantly impacted traditional workflows, offering enhanced efficiency, flexibility, and safety in diverse operational settings. The development of AMRs stems from the increasing demand for automation solutions to address challenges such as labor shortages, rising operational costs, and the need for faster and more accurate task execution. Unlike traditional industrial robots confined to fixed locations or predefined paths, AMRs possess the capability to navigate complex environments, adapt to changing conditions, and collaborate seamlessly with humans and other machines. The significance of research in the field of autonomous mobile robots (AMRs) extends across various domains, reflecting their transformative impact on industries, economies, and society as a whole. Understanding the importance of research in this field is essential for harnessing the full potential of AMRs and addressing the challenges and opportunities they present. Technological Advancements: Research in AMRs drives technological advancements, leading to the development of more sophisticated sensors, AI algorithms, and navigation systems. These advancements enhance the capabilities of AMRs, enabling them to operate in complex and dynamic environments with greater autonomy, efficiency, and safety. Industrial Automation: AMRs play a crucial role in industrial automation, revolutionizing manufacturing, warehousing, and logistics operations. Research in this area focuses on optimizing AMR deployment, task allocation, and coordination to improve productivity, reduce costs, and streamline processes in diverse industrial settings. Robo Navigator 3000, Auto Rover X, Smart Path Pro, Nav Bot Elite, Intelli Scout. Battery Life (hours), Navigation Accuracy (%), Maintenance Requirement (Rating), Noise Level (dB). The results indicate that Smart Path Pro achieved the highest rank, while Auto Rover X received the lowest rank being attained. “The value of the dataset for Autonomous Mobile Robot lies, according to the Weighted Sum Method (WSM), demonstrates that Smart Path Pro achieves the highest ranking.”

Aiming at the problem of navigation in unknown environment of autonomous mobile robot, the self-made mobile robot was taken as research object. The integrated navigation technology was adapted to integrate the SLAM navigation system, DR navigation system and MEMS micro inertial navigation system possessed by the studied robot to construct the SLAM/DR/MEMS integrated navigation system of mobile robot. The experiments were carried to test the method, and the results show that the precision of navigation and location of mobile robot was developed by the integrated navigation scheme.

This paper deals with navigation of mobile device in outdoor and indoor environment by only navigation system or application. In the paper, the navigation system is proposed in the light of seamless navigation service. Main parts of the system from positioning point of view are based on GPS and WifiLOC system. WifiLOC is an indoor positioning system based on Wi-Fi technology. The proposal of the system will be described in detail. The system is implemented at the University of Zilina as a pilot, noncommercial project; therefore it is called University Mobile Navigation System (UMNS). The navigation system can be characterized as real-time system, that is, the system operations cannot be significantly delayed. Since delay of the system depends significantly on communication platform used for map information downloading or communication with the localization server. We decided to investigate an impact of the used communication platform on the time needs for some of the functions implemented in navigation system. Measurements were performed in the real-world application. Next experiment is focused on testing of the accuracy of used indoor positioning system. Outdoor positioning accuracy is not tested because GPS is utilized in outdoor, and this system was already exhaustively investigated.