scholarly journals Framework for Fast Experimental Testing of Autonomous Navigation Algorithms

2019 ◽  
Vol 9 (10) ◽  
pp. 1997 ◽  
Author(s):  
Miguel Á. Muñoz–Bañón ◽  
Iván del Pino ◽  
Francisco A. Candelas ◽  
Fernando Torres
Keyword(s):  
Autonomous Navigation ◽  
Mobile Robotics ◽  
Experimental Testing ◽  
Autonomous Systems ◽  
Experimental Tests ◽  
Basic Structure ◽  
Satellite System ◽  
Outdoor Environments ◽  
Global Navigation Satellite ◽  
Abstraction Levels

Research in mobile robotics requires fully operative autonomous systems to test and compare algorithms in real-world conditions. However, the implementation of such systems remains to be a highly time-consuming process. In this work, we present an robot operating system (ROS)-based navigation framework that allows the generation of new autonomous navigation applications in a fast and simple way. Our framework provides a powerful basic structure based on abstraction levels that ease the implementation of minimal solutions with all the functionalities required to implement a whole autonomous system. This approach helps to keep the focus in any sub-problem of interest (i.g. localization or control) while permitting to carry out experimental tests in the context of a complete application. To show the validity of the proposed framework we implement an autonomous navigation system for a ground robot using a localization module that fuses global navigation satellite system (GNSS) positioning and Monte Carlo localization by means of a Kalman filter. Experimental tests are performed in two different outdoor environments, over more than twenty kilometers. All the developed software is available in a GitHub repository.

Autonomous Navigation of a Mobile Robot Based on GNSS/DR Integration in Outdoor Environments

2014 ◽  
Vol 26 (2) ◽  
pp. 214-224 ◽  
Author(s):  
Taro Suzuki ◽  
◽  
Mitsunori Kitamura ◽  
Yoshiharu Amano ◽  
Nobuaki Kubo ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Autonomous Navigation ◽  
Dead Reckoning ◽  
Satellite System ◽  
Mobile Robot Navigation ◽  
Observation Data ◽  
Multipath Mitigation ◽  
Ir Camera ◽  
Outdoor Environments ◽  
Global Navigation Satellite

This paper describes the development of a mobile robot system and an outdoor navigationmethod based on global navigation satellite system (GNSS) in an autonomous mobile robot navigation challenge, called the Tsukuba Challenge, held in Tsukuba, Japan, in 2011 and 2012. The Tsukuba Challenge promotes practical technologies for autonomous mobile robots working in ordinary pedestrian environments. Many teams taking part in the Tsukuba Challenge used laser scanners to determine robot positions. GNSS was not used in localization because its positioning has multipath errors and problems in availability. We propose a technique for realizing multipath mitigation that uses an omnidirectional IR camera to exclude “invisible” satellites, i.e., those entirely obstructed by a building and whose direct waves therefore are not received. We applied GPS / dead reckoning (DR) integrated based on observation data from visible satellites determined by the IR camera. Positioning was evaluated during Tsukuba Challenge 2011 and 2012. Our robot ran the 1.4 km course autonomously and evaluation results confirmed the effectiveness of our proposed technique and the feasibility of its highly accurate positioning.

scholarly journals Performance evaluation of multi-GNSSs navigation in super synchronous transfer orbit and geostationary earth orbit

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Tao Shi ◽  
Xuebin Zhuang ◽  
Liwei Xie
Keyword(s):  
Global Navigation Satellite System ◽  
Autonomous Navigation ◽  
Satellite System ◽  
Earth Orbit ◽  
Navigation Satellite ◽  
Geostationary Earth Orbit ◽  
Beidou Navigation Satellite System ◽  
Transfer Orbit ◽  
High Orbit ◽  
Global Navigation Satellite

AbstractThe autonomous navigation of the spacecrafts in High Elliptic Orbit (HEO), Geostationary Earth Orbit (GEO) and Geostationary Transfer Orbit (GTO) based on Global Navigation Satellite System (GNSS) are considered feasible in many studies. With the completion of BeiDou Navigation Satellite System with Global Coverage (BDS-3) in 2020, there are at least 130 satellites providing Position, Navigation, and Timing (PNT) services. In this paper, considering the latest CZ-5(Y3) launch scenario of Shijian-20 GEO spacecraft via Super-Synchronous Transfer Orbit (SSTO) in December 2019, the navigation performance based on the latest BeiDou Navigation Satellite System (BDS), Global Positioning System (GPS), Galileo Navigation Satellite System (Galileo) and GLObal NAvigation Satellite System (GLONASS) satellites in 2020 is evaluated, including the number of visible satellites, carrier to noise ratio, Doppler, and Position Dilution of Precision (PDOP). The simulation results show that the GEO/Inclined Geo-Synchronous Orbit (IGSO) navigation satellites of BDS-3 can effectively increase the number of visible satellites and improve the PDOP in the whole launch process of a typical GEO spacecraft, including SSTO and GEO, especially for the GEO spacecraft on the opposite side of Asia-Pacific region. The navigation performance of high orbit spacecrafts based on multi-GNSSs can be significantly improved by the employment of BDS-3. This provides a feasible solution for autonomous navigation of various high orbit spacecrafts, such as SSTO, MEO, GEO, and even Lunar Transfer Orbit (LTO) for the lunar exploration mission.

scholarly journals Evaluation of the Integrity Risk for Precise Point Positioning

2021 ◽  
Vol 14 (1) ◽  
pp. 128
Author(s):  
Bing Xue ◽  
Yunbin Yuan ◽  
Han Wang ◽  
Haitao Wang
Keyword(s):  
Precise Point Positioning ◽  
Time Window ◽  
Experimental Tests ◽  
Satellite System ◽  
Worst Case ◽  
Zero Bias ◽  
Point Positioning ◽  
Integrity Risk ◽  
Global Navigation Satellite ◽  
The Impact

Global Navigation Satellite System (GNSS) Precise Point Positioning (PPP) is an attractive positioning technology due to its high precision and flexibility. However, the vulnerability of PPP brings a safety risk to its application in the field of life safety, which must be evaluated quantitatively to provide integrity for PPP users. Generally, PPP solutions are processed recursively based on the extended Kalman filter (EKF) estimator, utilizing both the previous and current measurements. Therefore, the integrity risk should be qualified considering the effects of all the potential observation faults in history. However, this will cause the calculation load to explode over time, which is impractical for long-time missions. This study used the innovations in a time window to detect the faults in the measurements, quantifying the integrity risk by traversing the fault modes in the window to maintain a stable computation cost. A non-zero bias was conservatively introduced to encapsulate the effect of the faults before the window. Coping with the multiple simultaneous faults, the worst-case integrity risk was calculated to overbound the real risk in the multiple fault modes. In order to verify the proposed method, simulation and experimental tests were carried out in this study. The results showed that the fixed and hold mode adopted for ambiguity resolution is critical to an integrity risk evaluation, which can improve the observation redundancy and remove the influence of the biased predicted ambiguities on the integrity risk. Increasing the length of the window can weaken the impact of the conservative assumption on the integrity risk due to the smoothing effect of the EKF estimator. In addition, improving the accuracy of observations can also reduce the integrity risk, which indicates that establishing a refined PPP random model can improve the integrity performance.

scholarly journals Performance Analysis of Network-RTK Techniques for Drone Navigation considering Ionospheric Conditions

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Tae-Suk Bae ◽  
Minho Kim
Keyword(s):  
Autonomous Navigation ◽  
Low Cost ◽  
Satellite System ◽  
Test Results ◽  
Static Mode ◽  
Fixed Integer ◽  
Gnss Receivers ◽  
Global Navigation Satellite ◽  
Ionospheric Condition ◽  
Mapping Purpose

Recently, an accurate positioning has become the kernel of autonomous navigation with the rapid growth of drones including mapping purpose. The Network-based Real-time Kinematic (NRTK) system was predominantly used for precision positioning in many fields such as surveying and agriculture, mostly in static mode or low-speed operation. The NRTK positioning, in general, shows much better performance with the fixed integer ambiguities. However, the success rate of the ambiguity resolution is highly dependent on the ionospheric condition and the surrounding environment of Global Navigation Satellite System (GNSS) positioning, which particularly corresponds to the low-cost GNSS receivers. We analyzed the effects of the ionospheric conditions on the GNSS NRTK, as well as the possibility of applying the mobile NRTK to drone navigation for mapping. Two NRTK systems in operation were analyzed during a period of high ionospheric conditions, and the accuracy and the performance were compared for several operational cases. The test results show that a submeter accuracy is available even with float ambiguity under a favorable condition (i.e., visibility of the satellites as well as stable ionosphere). We still need to consider how to deal with ionospheric disturbances which may prevent NRTK positioning.

scholarly journals Moving Target Positioning Based on a Distributed Camera Network

2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
Long Zhao ◽  
Zhen Liu ◽  
Tiejun Li ◽  
Baoqi Huang ◽  
Lihua Xie
Keyword(s):  
Low Cost ◽  
Satellite System ◽  
Outdoor Environment ◽  
Moving Target ◽  
Camera Network ◽  
World Coordinate System ◽  
Outdoor Environments ◽  
Global Navigation Satellite ◽  
Systematic Framework ◽  
Indoor And Outdoor

We propose a systematic framework for moving target positioning based on a distributed camera network. In the proposed framework, low-cost static cameras are deployed to cover a large region, moving targets are detected and then tracked using corresponding algorithms, target positions are estimated by making use of the geometrical relationships among those cameras after calibrating those cameras, and finally, for each target, its position estimates obtained from different cameras are unified into the world coordinate system. This system can function as complementary positioning information sources to realize moving target positioning in indoor or outdoor environments when global navigation satellite system (GNSS) signals are unavailable. The experiments are carried out using practical indoor and outdoor environment data, and the experimental results show that the systematic framework and inclusive algorithms are both effective and efficient.

scholarly journals UAV Framework for Autonomous Onboard Navigation and People/Object Detection in Cluttered Indoor Environments

2020 ◽  
Vol 12 (20) ◽  
pp. 3386
Author(s):  
Juan Sandino ◽  
Fernando Vanegas ◽  
Frederic Maire ◽  
Peter Caccetta ◽  
Conrad Sanderson ◽  
...  
Keyword(s):  
Autonomous Navigation ◽  
Communication Systems ◽  
Satellite System ◽  
Human Operator ◽  
Indoor Environments ◽  
Flight Operations ◽  
Markov Decision ◽  
Indoor Scenarios ◽  
Global Navigation Satellite ◽  
Partially Observable

Response efforts in emergency applications such as border protection, humanitarian relief and disaster monitoring have improved with the use of Unmanned Aerial Vehicles (UAVs), which provide a flexibly deployed eye in the sky. These efforts have been further improved with advances in autonomous behaviours such as obstacle avoidance, take-off, landing, hovering and waypoint flight modes. However, most UAVs lack autonomous decision making for navigating in complex environments. This limitation creates a reliance on ground control stations to UAVs and, therefore, on their communication systems. The challenge is even more complex in indoor flight operations, where the strength of the Global Navigation Satellite System (GNSS) signals is absent or weak and compromises aircraft behaviour. This paper proposes a UAV framework for autonomous navigation to address uncertainty and partial observability from imperfect sensor readings in cluttered indoor scenarios. The framework design allocates the computing processes onboard the flight controller and companion computer of the UAV, allowing it to explore dangerous indoor areas without the supervision and physical presence of the human operator. The system is illustrated under a Search and Rescue (SAR) scenario to detect and locate victims inside a simulated office building. The navigation problem is modelled as a Partially Observable Markov Decision Process (POMDP) and solved in real time through the Augmented Belief Trees (ABT) algorithm. Data is collected using Hardware in the Loop (HIL) simulations and real flight tests. Experimental results show the robustness of the proposed framework to detect victims at various levels of location uncertainty. The proposed system ensures personal safety by letting the UAV to explore dangerous environments without the intervention of the human operator.

scholarly journals Constrained ESKF for UAV Positioning in Indoor Corridor Environment Based on IMU and WiFi

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 391
Author(s):  
Zhonghan Li ◽  
Yongbo Zhang
Keyword(s):  
Data Fusion ◽  
Autonomous Navigation ◽  
Low Cost ◽  
Optimization Method ◽  
Satellite System ◽  
Fusion Algorithm ◽  
K Nearest Neighbors ◽  
Positioning Accuracy ◽  
Spatial Features ◽  
Global Navigation Satellite

The indoor autonomous navigation of unmanned aerial vehicles (UAVs) is the current research hotspot. Unlike the outdoor broad environment, the indoor environment is unknown and complicated. Global Navigation Satellite System (GNSS) signals are easily blocked and reflected because of complex indoor spatial features, which make it impossible to achieve positioning and navigation indoors relying on GNSS. This article proposes a set of indoor corridor environment positioning methods based on the integration of WiFi and IMU. The zone partition-based Weighted K Nearest Neighbors (WKNN) algorithm is used to achieve higher WiFi-based positioning accuracy. On the basis of the Error-State Kalman Filter (ESKF) algorithm, WiFi-based and IMU-based methods are fused together and realize higher positioning accuracy. The probability-based optimization method is used for further accuracy improvement. After data fusion, the positioning accuracy increased by 51.09% compared to the IMU-based algorithm and by 66.16% compared to the WiFi-based algorithm. After optimization, the positioning accuracy increased by 20.9% compared to the ESKF-based data fusion algorithm. All of the above results prove that methods based on WiFi and IMU (low-cost sensors) are very capable of obtaining high indoor positioning accuracy.

Integrated Navigation Models of a Mobile Fodder-Pushing Robot Based on a Standardized Cow Husbandry Environment

2020 ◽  
Vol 63 (2) ◽  
pp. 221-230
Author(s):  
Shenghui Yang ◽  
Shenghao Liang ◽  
Yongjun Zheng ◽  
Yu Tan ◽  
Zhang Xiao ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Time Course ◽  
Satellite System ◽  
Integrated Navigation ◽  
Multiple Sensors ◽  
Motion Models ◽  
Outdoor Environments ◽  
Global Navigation Satellite ◽  
Smart Mobile

HighlighIntegrated navigation models for a two-wheel robot were specifically developed for a semi-enclosed environment.A combination of Kalman filter and fuzzy control system was developed with mathematical models.Real-time pose estimate and adjustment of perturbances due to feeding cows and fodder resistance were achieved.Abstract. As part of welfare feeding, standardized feeding is commonly used for cows in confined operations. Due to the strict facility requirements, smart mobile robots have been specifically developed to address these semi-enclosed environments. Their navigation is based on electromagnetic sensors with magnetic tapes, which does not easily allow route changes and other abilities afforded by the newer integrated sensors and Global Navigation Satellite System (GNSS) guidance packages available on large agricultural machinery in outdoor environments. This article proposes a system of integrated navigation using multiple sensors, which was used for a two-wheel-drive robot operating in the standardized environment of a cow husbandry facility. The developed system combined incremental encoders, ultrasonic sensors, and a gyroscope to determine parameters such as course angle and covered distance. A fuzzy self-adaption Kalman filter was applied to integrate these parameters and estimate the robot pose, so that the robot could achieve real-time course adjustment during operation. Experimental trials indicated that the real-world route was highly consistent with the set route. Moreover, the cross-track error was =0.10 m at a travel velocity of 0.2 m s-1, indicating that perturbances due to feeding cows and fodder resistance had little interference on the movement of the robot, and the models were robust and accurate. This novel integrated sensing system with a fuzzy self-adaption Kalman filter and derived models was able to guide real-time robot operations in a modern cow husbandry environment without the need for magnetic tapes. Keywords: Kalman filter, Integrated navigation, Motion models, Pose estimate, Welfare feeding.

scholarly journals Performance analysis of GNSS/INS loosely coupled integration systems under GNSS signal blocking environment

2020 ◽  
Vol 206 ◽  
pp. 02013
Author(s):  
Mengke Wang ◽  
Peidong Yu ◽  
Yunzhi Li
Keyword(s):  
Autonomous Navigation ◽  
Inertial Navigation System ◽  
Satellite System ◽  
Sampling Frequency ◽  
Navigation Satellite ◽  
Navigation Systems ◽  
Post Processing ◽  
Positioning Accuracy ◽  
Loosely Coupled ◽  
Global Navigation Satellite

Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) are the most widely used navigation systems at present. Aiming at the limitations of a single system application, this paper uses kalman filter to fuse the pose information provided by GNSS and INS, respectively. GNSS has the characteristics of being easily affected by the environment but with high absolute positioning accuracy. INS has the characteristics of high sampling frequency and autonomous navigation, but the error accumulates with time. Combining the advantages of the two systems to achieve the purpose of obtaining higher-precision pose information. In addition, aiming at the problem that GNSS/INS integration cannot provide continuous, stable and reliable navigation solutions under the GNSS signal blocking environment, a smoothing post-processing algorithm for GNSS/INS integration is studied. Through experimental verification, this algorithm can effectively improve the pose accuracy under GNSS signal blocking environment.

Multi-Region Scene Matching Based Localisation for Autonomous Vision Navigation of UAVs

2016 ◽  
Vol 69 (6) ◽  
pp. 1215-1233 ◽  
Author(s):  
Zhenlu Jin ◽  
Xuezhi Wang ◽  
Bill Moran ◽  
Quan Pan ◽  
Chunhui Zhao
Keyword(s):  
Autonomous Navigation ◽  
Satellite System ◽  
Affine Transformations ◽  
Vision Navigation ◽  
Scene Matching ◽  
Navigation Error ◽  
Image Pairs ◽  
Global Navigation Satellite ◽  
Colour Histograms ◽  
Correction System

A multi-region scene matching-based localisation system for automated navigation of Unmanned Aerial Vehicles (UAV) is proposed. This system may serve as a backup navigation error correction system to support autonomous navigation in the absence of a global positioning system such as a Global Navigation Satellite System. Conceptually, the system computes the location of the UAV by comparing the sensed images taken by an on board optical camera with a library of pre-recorded geo-referenced images. Several challenging issues in building such a system are addressed, including the colour variability problem and elimination of time-varying details from the pairs of images. The overall algorithm is an iterative process involving four sub-processes: firstly, exact histogram matching is applied to sensed images to overcome the colour variability issues; secondly, regions are automatically extracted from the sensed image where landmarks are detected via their colour histograms; thirdly, these regions are matched against the library, while eliminating inconsistent regions between underlying image pairs in the registration process; and finally the location of the UAV is computed using an optimisation procedure which minimises the localisation error using affine transformations. Experimental results demonstrate the proposed system in terms of accuracy, robustness and computational efficiency.

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