Environmentally-Friendly Navigation

The sidewalk is often located in complex environments such as buildings or covered by dense trees. Satellite navigation systems are susceptible to the surrounding environment, resulting in multipath effects, resulting in reduced positioning accuracy. Therefore, the global satellite navigation system (GNSS) and inertial navigation system (INS) based on kalman filter (KF) algorithm are studied, and the navigation algorithm and system model suitable for pedestrians are given. Considering that the pedestrian speed is low, the satellite navigation system obtains the carrier orientation information (heading angle), which also produces a large error. Therefore, the pedestrian dead reckoning (PDR) correction method is adopted to make the positional accuracy of the integrated navigation continuously stable. This paper proposes a pedestrian navigation system solution based on a single navigation device.

Speech recognition technology has potential for application to intelligent transportation systems (ITS) in the area of advanced traveler information systems (ATIS). ATIS areas which can greatly benefit from speech recognition are: pre-trip traffic information; en-route traffic information; route guidance; ride matching and reservation; transit information services; and traffic and transit management control centers. This paper concentrates on telephony applications of speech recognition to two ATIS applications, real-time traffic information and public transit information. We first discuss the general considerations involved in development of a multiline speech interface to a traffic or transit database, including system architecture and sizing, and the development of dialogs and grammars which are user friendly and efficient. We then discuss two prototype applications of this technology using BBN's HARK speaker-independent speech recognition product. The first is to SmartRoute System's SmarTraveler traveler information system, which was an FHWA Operational Test, and the second to Montgomery County Maryland's public transit system. These were small scale tests to help us develop the technology required for full-scale implementations, but showed that the speech interface was practical, and provided much improved ease of use and performance over conventional touch tone based interactive voice response (IVR) systems.

The aim of the work presented in this article is to develop a navigation system that allows a mobile robot to move autonomously in an indoor environment using perceptions of multiple events. A topological navigation system based on events that imitates human navigation using sensorimotor abilities and sensorial events is presented. The increasing interest in building autonomous mobile systems makes the detection and recognition of perceptions a crucial task. The system proposed can be considered a perceptive navigation system as the navigation process is based on perception and recognition of natural and artificial landmarks, among others. The innovation of this work resides in the use of an integration interface to handle multiple events concurrently, leading to a more complete and advanced navigation system. The developed architecture enhances the integration of new elements due to its modularity and the decoupling between modules. Finally, experiments have been carried out in several mobile robots, and their results show the feasibility of the navigation system proposed and the effectiveness of the sensorial data integration managed as events.

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.

Military Autonomous Underwater Vehicle (AUV) is utilized to search a mine under the sea. This paper presents design and performance verification of real-time hybrid navigation system for AUV. The navigation system uses Doppler Velocity Log (DVL) integration method to correct INS error in underwater. When the AUV is floated on the water, the accumulated error of navigation algorithm is corrected using position/velocity of GPS. The navigation algorithm is verified using 6 Degree Of Freedom (DOF) simulation, Program In the Loop Simulation (PILS). Finally, the experiments are performed in real sea environment to prove the reliability of real-time hybrid navigation algorithm.

The development of a navigation system for autonomous robotic sailing is a particularly challenging task since the sailboat robot uses unpredictable wind forces for its propulsion besides working in a highly nonlinear and harsh environment, the water. Toward solving the problems that appear in this kind of environment, we propose a navigation system which allows the sailboat to reach any desired target points in its working environment. This navigation system consists of a low-level heading controller and a short-term path planner for situations against the wind. For the low-level heading controller, a gain-scheduling proportional-integral (GS-PI) controller is shown to better describe the nonlinearities inherent to the sailboat movement. The gain-scheduling-PI consists of a table that contains the best control parameters that are learned/defined for a particular maneuver and perform the scheduling according to each situation. The idea is to design specialized controllers which meet the specific control objectives of each application. For achieving short-term path-planned targets, a new approach for optimization of the tacking maneuvering to reach targets against the wind is also proposed. This method takes into account two tacking parameters: the side distance available for the maneuvering and the desired sailboat heading when tacking. An optimization method based on genetic algorithm is used in order to find satisfactory upwind paths. Results of various experiments verify the validity and robustness of the developed methods and navigation system.

Precise navigation of autonomous underwater vehicles (AUV) is one of the most important challenges for marine applications. Current 2-dimensional (2D) navigation systems (algorithms) are effective to autonomous surface vehicles (ASV) operations, but still have some limitations for AUV navigation in 3D underwater environment. In this paper, a 3D range navigation algorithm is proposed that is potentially useful for small AUVs that have severe volume and power constraints. This algorithm enables an AUV to compute its trajectory in the presence of unknown currents and simultaneously estimate the currents by using range measurements from a single known location (beacon). The principal work here is the observability analysis of the proposed navigation system that characterizes observable AUV trajectories using novel approaches towards uniform observability of linear time varying systems. Then, implementation of the 3D range navigation algorithm as a discrete time extended Kalman filter is given based on the observability results and kinematic model of the system. Finally, a set of comparative simulation results verify the validity of the navigation algorithm and the system's observability related to trajectories.

This research aims to build mechanical system of Automated Guided Vehicle (AGV) and navigation system for AGV. The navigation system consists of RFID reader and rotary encoder. The testing result show that mechanical system of AGV was successfully finished. There is a mechanical problem on drive system of AGV. Each part of navigation system works well according to design. Line sensor can be used for path detection with the given threshold values. Encoder can be used to measure the speed of AGV with the maximum error accuracy less than 2 rpm. This result shows that every part of AGV is ready to run localization and navigation algorithm, even though, it needs to do some improvement on driving parts.

This paper introduces the mechanism analysis of the large beacon calibration errors how to decrease the navigation performance for the AUV (Autonomous Underwater Vehicle). Generally, when the vehicle using inertial navigation system is performing specific tasks in the water, the navigation error accumulates as time goes on; sometimes the navigation algorithm may fail because of a lot of disturbances, such as outliers, noisy measurement, missed measurement. In order to obtain the better navigation performance for underwater navigation system in real time, the underwater integrated navigation system has been constructed with the underwater acoustic and inertial navigation system. But it is hard to obtain the accurate initial beacon position for the acoustic system in the complex and unknown underwater environment, which will seriously deteriorate the navigation performance. For the sake of obtaining the better navigation performance, we analyze the reasons how the large calibration errors decrease the navigation performance. At last we also provide three feasible methods to online correct the calibration errors. The lake trial data further explain the mechanism analysis.

Analysis on the controlled nonlinear motion of a test bed AUV–SNUUV I

The aim of this study was to investigate the effect of changes in meteorological parameters and fuel composition on the emission rate of air pollutants in the vehicle fleet of the Tehran Metropolis. The results of this study can be used in management decisions to reduce the emission of air pollutants. In this paper, based on the international vehicle emission model and using mathematical equations, the effects of changing meteorological parameters and fuel composition on the emission of pollutants were modeled. The emission rates of CO, VOCs, and NOx pollutants were the most sensitive to the changes in meteorological parameters, respectively. Among all parameters studied in this research, the changes in sulfur level had the greatest effect on the emission of pollutants from the vehicle fleet of Tehran Metropolis. If the fuel was replaced with Euro 5 standard instead of Euro 3, the emission rates of CO, VOCs, NOx, PM, and SOx pollutants from the vehicle fleet of Tehran Metropolis would be reduced by 9%, 6%, 5%, 14%, and 90%, respectively. Managing and reducing the sources of production and emission of air pollution is one of the best ways to reduce the air pollution. In general, since the emission of pollutants from the fleet of Tehran Metropolis in the cold seasons of the year is greater than during hot seasons and the problem of air pollution is exacerbated by air stability, using Euro 5 fuel in cold seasons is one of the efficient ways to reduce the air pollution.

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.”

Common bus route navigation system provides distance and transferring information based on the user-determined start point and end point on the map. This paper presents the research and experiment of an advanced bus route navigation system in Beijing, which can additionally provide travel time information from start to end. The travel time is predicted based on statistical and real-time traffic information. In Beijing, about 10000 taxies send GPS data to a traffic information center to generate real-time traffic data, and the center accumulates the historical traffic data for generating statistical database. Field test of our system was carried out in January 2007, and the experiment result shows the real-time and statistical traffic information can be used not only for car navigation system, but also for public transit service. In our experiment, the average error rate of predicted travel time is about 10%, which is comparable to car navigation system.

Navigation systems for ships mainly include inertial navigation system (INS), “BeiDou” satellite navigation system, Global Positioning System (GPS), and Doppler navigation system and so on. The characteristics of each system are different. In order to improve the accuracy and fault-tolerant ability of naval-ship navigation system, an integrated navigation algorithm based on Measurement-based adaptive Kalman filtering algorithm (MAKF), improved Suge-Husa algorithm and non-reset federated Kalman filter structure is proposed in this paper. The algorithm uses the short-term high precision of the inertial navigation system and the prior velocity information of the ship to adaptively estimate the measurement noise characteristics of the satellite navigation system. When the satellite navigation system is unable to locate, the INS and the log are combined with the help of the prior velocity information. The simulation results show that the algorithm can effectively exploit the characteristics of each navigation system, and can output high-precision navigation results even when the GPS/BeiDou satellite navigation system fails.

The objective of this work is to introduce an autonomous Hovercraft with minimum Energy consumption. To achieve this goal, a computer simulation in addition to practical testing of a simple hovercraft model have been carried out. A mathematical model for the hovercraft is considered and simulated in the MATLAB/SIMULINK environment. A navigation system with Global Positioning System (GPS) integrated with an Inertial Measurement Unit (IMU) sensors are used to monitor the speed, position, and the direction of the hovercraft for the autonomous operation. Two motors with propellers are used for the lifting and thrust systems, while a third one is used for the rudder movement that controls the direction. Specific resistance (Ɛ) index is used to test the hovercraft performance. The results of the practical experiments of moving the hovercraft between two and three points are compared with that of the computer simulation. The distance error from the actual target has been found to be in the range of about 18%, which proves the idea. It should be noted that this significant error is due to the fact that the GPS considered is a low price commercial one and using more accurate one will result in more satisfactory results. Based on the computer simulation and practical testing results, a look up table has been prepared to help in deriving an algorithm for controlling the RPM of lifting and thrust Fans based on the minimum optimum specific resistance.