A Navigation System for Crowd Flow Control by Controlling Information Presentation

Joint Charging and Routing Optimization for Electric Vehicle Navigation Systems

To solve the localization failure problem of terrain-aided navigation (TAN) system of the autonomous underwater vehicle (AUV) caused by large area of underwater flat terrain in the Arctic, a navigation system with relocation part is constructed to enhance the robustness of localization. The system uses particle filter to estimate the AUV’s position and reduce the nonlinear noise disturbance, and the prior motion information is added to avoid the mismatching caused by the similar altitude of low-resolution map. Based on the estimate data and the measured altitude data, the normalized innovation square (NIS) is used to evaluate the differentiation of terrain sequence, and the differentiation is used as a judgment of whether the AUV is in the switch location. A simulation experiment is carried out on the 500 m resolution underwater map of the Arctic. The results show that adding the prior motion information can restrain the divergence of the estimator; NIS can accurately reflect the sharp change of terrain sequence. After the relocation process, the AUV can still maintain the positioning accuracy within 2 km after running 50 km in the area including flat and rough terrain. This research solves the problem of localization errors in the Arctic flat terrain in the system level and provides a solution for the application of underwater navigation in the Arctic.

This work presents an improvement of an inertial-Doppler underwater navigation system for unmanned underwater vehicles (UUV) using a complementary range sonar. The inertial-Doppler navigation system is generally based on an inertial measurement unit (IMU) and a Doppler velocity log (DVL) accompanying a magnetic compass and a depth sensor. Although conventional navigation systems update bias errors of the inertial sensors, scale effects of the DVL, the estimated position slowly drifts as time passes. This paper proposes a measurement model with additional range sonar to improve the performance of the IMU-DVL navigation system for underwater vehicles. The proposed navigation model based on inertial navigation systems includes the scale effects, the bias errors of the DVL, and the error model of the range sonar, where the order of the system states is 21. An extended Kalman filter was adopted to propagate the error covariance, updated the measurement errors and correct the state equation when the external measurements are available. Simulation conducted with the 6-d.o.f. equations of motion of an AUV in lawn-mowing survey mode illustrates the effectiveness of the IMU-DVL navigation system assisted by the additional range measurement.

It is possible for the visually impaired to walk their known area independently by using a white cane. However, in their unknown area, they cannot walk without help of others even if they can use a white cane very well. In such cases, not only a visually impaired person but he/she who assists them receives many stress in dairy activity. Therefore, many types of independent walking assist system for the visually impaired are developing. Our objective of this study is the development of an auto navigation system for the visually impaired which assists independent walking of them. This system measures the position of a visually impaired person (a user) by using GPS (Global Positioning System) and navigates him/her to the destination like as a car navigation system. In our system, a location data is stored in the map database and a position of user are analyzed and optimal route for a user to the destination is calculated. Our system guides a user to the destination along this route, and notifies the user of this route and some information for the safety walking by artificial voice. Three normal subjects were tested with our navigation system. All subjects were blind folded by an eye mask and equipped a navigation system. As a result, our system worked well and all subjects were able to walk to the destination following guidance voice. So, we are developing a new navigation system which is installed on a cellular phone. In Japan, there are some cellular phones that include GPS. And most of application programs for cellular phone are written by JAVA. Therefore, GPS included in a cellular phone will be used and every functions of the navigation system will be written by JAVA. In this paper, we also reports about this new navigation system.

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:

Off-Road Fast Navigation Ground Vehicle Speed Planning Using Range Data

Aiming at solving the navigation and obstacle avoidance of the unmanned vehicle,the multi sensor data fusion technology and unmanned vehicle obstacle avoidance navigation algorithm were studied profoundly. According to the requirements of the application of unmanned vehicle navigation and obstacle avoidance system, multisensor data fusion technology was applied to unmanned vehicle navigation and obstacle avoidance control system. In addition, A*VFF navigation and obstacle avoidance algorithm based on fuzzy neural network was improved. Finally, through the construction of the simulation platform, simulation experiment of the unmanned vehicle obstacle avoidance navigation was completed, and a better route was planned for unmanned vehicle in a more complex environment. The results showed that it realized the autonomous navigation of unmanned vehicle and obstacle avoidance function. Based on the above findings, it is concluded that the application of artificial intelligence detection system has good performance.

In our current research, we are developing a practical mobile vehicle navigation system which is capable of controlling multiple vehicles on the general environment. In order to realize the practical use, navigation system should have high accuracy and be low cost. Therefore, in this paper, we propose the vehicle navigation system which realizes vehicles navigation using high accuracy localization scheme by ceiling cameras with infrared filters and LED markers. Our key ideas are two topics. One topic is system integration which consists of high accuracy recognition system for LED marker and AGV controller with low cost. The other topic is novel AGV navigation scheme with high accuracy under occlusion condition. For practical use, mobile vehicles are required to continue its tasks with safety and high accuracy on temporary occlusion condition. Our developed system can navigate target vehicle based on estimation odometry error with simple algorithm. The performance of our proposed control system is experimented with our prototype.

The car navigation service has opened through the global positioning system (GPS). People's lives have changed through car navigation services, and this service is indispensable nowadays. GPS provides reliable location accuracy in open space, but its location accuracy is degraded where multipath and other error factors are present. In particular, since GPS signals must be received in order to calculate the position, the navigation system cannot calculate the position in an indoor space such as underground parking lot, subway or tunnel where GPS signals are cut off. When car navigation is performed in an indoor parking lot where GPS signals are not transmitted, the user cannot calculate the current location, so the user has to go out by relying on the traffic sign because he does not know the current location. Also, when the user leave the parking lot, the car navigation system has problems. In an urban canyon, it is difficult to calculate accurate current position due to multipath. Because the route is calculated through the current location and route, the car navigation system provides the wrong route to the user. In addition, when entering a tunnel while using car navigation, the car navigation system calculates the position using the initial tunnel entry velocity estimated by the GPS. As the speed of the user increases or decreases, the position error also increases. Then the position suddenly changes when passing through the tunnel. And it is difficult to provide a proper guidance at the branch road or exit in the tunnel because of the positioning error. People are demanding a complete car navigation service, but the current navigation service provides the wrong route and position information to the user where the GPS signal quality is poor. In order to solve the above problem, in this paper, we have studied to improve the user's position accuracy by using LTE signal and mobile phone internal sensor. In general, observed time difference of arrival (OTDOA) technique is applied for the LTE based positioning. Because this method gives tens of meters of error, it is difficult to be used for the car navigation service. In our research, we estimate the position using the LTE signal strength. The difference from conventional fingerprinting method is that instead of using only current signal strength, the signal strength pattern accumulated while moving is utilized to estimate the user position. The signal strength increases as the user approach the access point (AP), and decreases as the user move away from the AP. Therefore, when a user moves and stores signal strength with respect to a specific AP, a pattern that could occur only in the corresponding region is saved. There are many similar candidates in the database when using only the current signal strength. However, when using a pattern, similar candidates exist only around the true position. And, when using several LTE signals, the positioning accuracy could be increased. Since the user receives multiple LTE signals, the pattern becomes surface. The surface is compared with the database, and the position with the highest correlation value is estimated as the current position. We call this method surface correlation (SC). In order to verify the performance of the proposed method, experiments were conducted in the Gangnam beltway tunnel in Seoul. The testbed consisted of three tunnel and total distance is almost 11.8 Km. In the tunnel, repeater and several antennas are installed to widen the area of the LTE signal. When an LTE signal is received by the vehicle, the largest signal strength is received every time when the vehicle passes the nearest antenna. As a result, for a specific LTE signal, when the vehicle has passed through the LTE signal region, a unique pattern with multiple peaks is generated. In order to estimate the position, we first estimate the position with the SC technique. Then, the estimated position is corrected by comparing the peak of the currently received pattern with the peak of the database. Through this method, the position was estimated with an error of less than 50meter in the tunnel. Conclusion In this research, the LTE signal strength based positioning technology is presented. Especially, this technology, SC, provides the positioning performance that could be used for the car navigation in the tunnel where GPS signal is blocked. We expected that user would be use the accurate car navigation service through this technology in the GPS challenging environment.

현재 육상에서의 GPS는 자동차 항법체계(Car Navigation System)와 개인휴대용 단말기를 이용한 위치정보체계 등이 활발히 활용되고 계속 연구되고 있는 추세에 있지만 해양에서는 그렇지 못한 실정으로 원래 GPS는 선박의 항해용 목적으로 개발된 것이지만, 해상에서 선박의 운항에 정확도는 크게 어려움을 주지 않는다는 이유로 선박의 항로확인을 위한 근사적인 위치의 표현만을 수행해왔다. 최근 항만이 복잡해지고 선박의 증가로 해양에서도 선박의 정밀한 유도가 필요하게 되었다. 본 연구에서는 GPS에서 사용하고 있는 타원체인 WGS84 타원체의 자리좌표를 우리나라에서 사용하고 있는 타원체인 BESSEL 타원체의 지리좌표로 변환하는 요소인 3-parameter에 의한 타원체변환알고리즘 및 육도와 해도에서 사용하고 있는 평면 직교좌표계인 TM투영과 UTM투영의 알고리즘제작 하였다. 또한, 현재 항법용 GPS 센서가 가지고 있는 정확도를 검증하기 위해 GPS의 정적측량, 동적측량에 의한 오차량을 비교검토하였으며, 선박의 항로추적을 위해서 선박에서 수신된 위치 정보의 편차량을 분석하였다. 이렇게 검토된 자료를 기본으로 하여 실시간 선박항로를 추적할 수 있는 저가의 항로추적시스템 개발을 본 연구의 최종목표로 연구를 수행하였다. The most great GPS is working on CNS(Car navigation System) and Personal terminal, but ocean has not been. First studied GPS is sailing to ship but We have been connivance that The ship is not need detail navigation. Lately, harbor facilities and transportation service in harbors are complication. We have need accurate ship navigation system. In this paper, author developed algorithms of ellipsoid conversion between WGS84 and BESSEL and develope algorithms of map projection between ellipsoid coordinate system, and plane cartesian system. Author used 3-parameter in covering ellipsoids and used TM and UTM projection in converting between ellipsoid and plane cartesian coordinate. And author analyzed errors through static surveying and dynamic surveying of GPS for proving accuracy of GPS sensor, Furthermore author analyzed deflection error of received position. Finally author developed real time ship navigation system using cheep GPS sensor.

In various applications of land vehicle navigation and automatic guidance systems, Global Navigation Satellite System/Inertial Measurement Unit (GNSS/IMU) positioning performance crucially depends on the attitude determination accuracy affected by gyro and accelerometer bias instabilities. Traditional bias estimation approaches based on the Kalman filter suffer from implementation complexity and require non-intuitive tuning procedures. In this paper we propose, as an alternative, a simple observer that estimates inertial sensor biases exclusively in terms of quantities with obvious geometrical meaning. By this, any multidimensional vector-matrix operations are avoided and observer tuning is substantially simplified. The observer has been successfully tested in a farming vehicle navigation system.

The advances in microelectronics foster the Unmanned Aerial Vehicles (UAVs) to be used in many civil and academic applications that require higher levels of autonomy. Therefore, the navigation systems are considered one of the main subjects to study. This paper deals with the problem of estimating the pose of the UAV in the 3D world. In which, a vision-based navigation system using onboard monocular downward looking camera is proposed. The proposed system is based on a SIFT detector and FREAK descriptor which can keeps the performance of the feature matching and decrease the computational time. The system has been evaluated with real flight tests and the obtained results have been compared with the results from the DGPS.

The most driving purpose is to traverse to the destination safely, efficiently, and comfortably. Two types of approaches could achieve the goals, including the static and dynamic approaches. In the static aspect, vehicles use the static road and traffic information to navigate. Conversely, in the dynamic aspect, vehicles adopt the dynamic information instead. However, both of the two approaches first require getting the vehicle’s location and then map the position on an e-map. Thus, this chapter first introduces some important vehicle location determination algorithms: the dead reckoning and global position system algorithms, in which the precision of location technologies are compared. Then, the map-matching algorithm is described in detail. Finally, various vehicle navigation approaches are detailed, in which the important topics include: the navigation architecture, the navigation routing algorithm, and navigation applications.

Abstract. Recent years have witnessed the rapid development of navigation systems. They gradually become the default way of navigating in our daily life. Despite benefiting from their convenience, we may be negatively influenced by them (Parush et al. 2007). Current navigation systems usually draw too much of users' attention to the mobile devices, which leads to users’ lacking interaction with the environment. It is often the case that people easily forget travelled route and get lost without navigation systems (Reilly et al. 2008). Empirical study shows that by dividing attention, navigation systems impair users’ spatial memory, and thus impact their navigation skills (Gardony et al. 2013). Since a higher engagement with the environment can improve users’ spatial knowledge acquisition without decreasing efficiency (Brügger et al. 2017), navigation systems should incorporate important environmental features.As objects of visual, cognitive or structural importance (Sorrows & Hirtle 1999), landmarks have been extensively studied, and considered as an important environmental feature to be incorporated into navigation systems (Nuhn & Timpf 2018, Richter 2017, Duckham et al. 2010, Raubal & Winter 2002). They serve as references to provide users with more natural and pleasant navigating experience (Richter & Winter 2014). However, most of landmark-related studies are in outdoor environment. Although researchers have attempted to classify and assess indoor landmarks (Ohm 2014, Viaene 2014), it is still very difficult to determine suitable ones. As a subset of semantic indoor landmarks, existing navigational aids (e.g. signs and floor plans) are designed to assist navigation. Among them, signs are most preferred by users (Wang et al. 2017). Being the signs that give necessary information to route users to possible destinations, directional signs contribute most to wayfinding (Gibson 2009). With route information explicitly presented by them, they are relevant environmental features to be incorporated into indoor navigation systems.In this study, we therefore propose to incorporate directional signs into indoor navigation systems, in order to guide users’ attention back to the environment, to facilitate their spatial learning and improve their spatial knowledge acquisition.We incorporated directional signs into a proof-of-concept indoor navigation system. First, we integrated directional signs and their semantics into a graph-based model. A directional sign was modelled as a node. Each destination on the sign was linked with the sign by an edge. In order to differentiate it from the normal edge, it was marked as semantic edge. Thus, the semantics of the sign was modelled directly into the graph, instead of being stored as attributes in a previous version of our study (Wang & Gartner 2018). Then, a top-K shortest path algorithm was employed to find candidate routes (Yen 1971). In the end, the route with the largest coverage of semantic edges was suggested to the user. The users would then be explicitly guided to follow the directional signs in the environment.In the next step, we will evaluate the efficiency and effectiveness of the proposed navigation system by an empirical study with human subjects. Within the experiment, we will also investigate on whether this kind of guidance can succeed in drawing users’ attention back to the environment and support their spatial knowledge acquisition.

Emerging automotive engineering solutions extensively rely on navigation tools. Shortest path evaluation is core competency of optimal path planning, one of the most critical component of any navigation application. Fibonacci heap variant of state-of-the-art Dijkstra’s algorithm solve single source shortest path problems in asymptotic O(E + V log V) worst-case time. In spite Fibonacci heap is theoretically dominant in time efficiency, it might not work much better empirically with real-world data, because although large and intricate, the real-world road network graphs are sparse and the graph topology greatly affects the performance of the algorithms. SGO algorithm, a more efficient approch to address single source shortest path problem, is applied to solve optimal path planning problem in automotive navigation applications. Each road segment of the road graph leads to a particular location thus will hold unique distance values all the time. The SGO calculates the total distance from start location to the end of the each road segment and evaluates shortest path based on these calculations. The SGO algorithm and the Fibonacci and Binary heap variants of Dijkstra’s algorithm were tested on real-world intercity and intra-city road network graphs to evaluate running time efficiency of the algorithms in automotive navigation systems. The experimental results show that the SGO algorithm outperformed both the variants. The empirical superiority of the SGO algorithm suggests its usefulness in automotive navigation systems.