Intelligent navigation algorithm of plant phenotype detection robot based on dynamic credibility evaluation

Abstract Unmanned aerial vehicles (UAVs) have become essential tools for exploring, mapping and inspection of unknown three-dimensional (3D) tunnel-like environments which is a very challenging problem. A computationally light navigation algorithm is developed in this paper for quadrotor UAVs to autonomously guide the vehicle through such environments. It uses sensors observations to safely guide the UAV along the tunnel axis while avoiding collisions with its walls. The approach is evaluated using several computer simulations with realistic sensing models and practical implementation with a quadrotor UAV. The proposed method is also applicable to other UAV types and autonomous underwater vehicles.

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A new visual/inertial integrated navigation algorithm based on sliding-window factor graph optimisation

Journal of Navigation ◽

10.1017/s0373463320000582 ◽

2020 ◽

pp. 1-17

Author(s):

Haiying Liu ◽

Jingqi Wang ◽

Jianxin Feng ◽

Xinyao Wang

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Prior Information ◽

Sliding Window ◽

Factor Graph ◽

Navigation Systems ◽

Integrated Navigation ◽

Sliding Windows ◽

Navigation Algorithm ◽

Almost All

Abstract Visual–Inertial Navigation Systems (VINS) plays an important role in many navigation applications. In order to improve the performance of VINS, a new visual/inertial integrated navigation method, named Sliding-Window Factor Graph optimised algorithm with Dynamic prior information (DSWFG), is proposed. To bound computational complexity, the algorithm limits the scale of data operations through sliding windows, and constructs the states to be optimised in the window with factor graph; at the same time, the prior information for sliding windows is set dynamically to maintain interframe constraints and ensure the accuracy of the state estimation after optimisation. First, the dynamic model of vehicle and the observation equation of VINS are introduced. Next, as a contrast, an Invariant Extended Kalman Filter (InEKF) is constructed. Then, the DSWFG algorithm is described in detail. Finally, based on the test data, the comparison experiments of Extended Kalman Filter (EKF), InEKF and DSWFG algorithms in different motion scenes are presented. The results show that the new method can achieve superior accuracy and stability in almost all motion scenes.

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Agoraphilic navigation algorithm in dynamic environment with obstacles motion tracking and prediction

Robotica ◽

10.1017/s0263574721000588 ◽

2021 ◽

pp. 1-19

Author(s):

H. S. Hewawasam ◽

M. Yousef Ibrahim ◽

Gayan Kahandawa ◽

T. A. Choudhury

Keyword(s):

Free Space ◽

Motion Tracking ◽

Dynamic Environment ◽

Experimental Tests ◽

Cluttered Environments ◽

Navigation Algorithm ◽

Prediction Module ◽

Future Space ◽

Free Spaces ◽

Advanced Development

Abstract This paper presents a new algorithm to navigate robots in dynamically cluttered environments. The proposed algorithm uses basic concepts of space attraction (hence the term Agoraphilic) to navigate robots through dynamic obstacles. The new algorithm in this paper is an advanced development of the original Agoraphilic navigation algorithm that was only able to navigate robots in static environments. The Agoraphilic algorithm does not look for obstacles (problems) to avoid but rather for a free space (solutions) to follow. Therefore, it is also described as an optimistic navigation algorithm. This algorithm uses only one attractive force created by the available free space. The free-space concept allows the Agoraphilic algorithm to overcome inherited challenges of general navigation algorithms. However, the original Agoraphilic algorithm has the limitation in navigating robots only in static, not in dynamic environments. The presented algorithm was developed to address this limitation of the original Agoraphilic algorithm. The new algorithm uses a developed object tracking module to identify the time-varying free spaces by tracking moving obstacles. The capacity of the algorithm was further strengthened by the new prediction module. Future space prediction allowed the algorithm to make decisions considering future growing/diminishing free spaces. This paper also includes a bench-marking study of the new algorithm compared with a recently published APF-based algorithm under a similar operating environment. Furthermore, the algorithm was validated based on experimental tests and simulation tests.

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TDMA Datalink Cooperative Navigation Algorithm Based on INS/JTIDS/BA

Electronics ◽

10.3390/electronics10070782 ◽

2021 ◽

Vol 10 (7) ◽

pp. 782

Author(s):

Shuo Cao ◽

Honglei Qin ◽

Li Cong ◽

Yingtao Huang

Keyword(s):

Distribution System ◽

Large Scale ◽

Satellite System ◽

Time Of Arrival ◽

Military Operations ◽

Position Information ◽

Cooperative Navigation ◽

Navigation Algorithm ◽

Geometric Dilution Of Precision ◽

Global Navigation Satellite

Position information is very important tactical information in large-scale joint military operations. Positioning with datalink time of arrival (TOA) measurements is a primary choice when a global navigation satellite system (GNSS) is not available, datalink members are randomly distributed, only estimates with measurements between navigation sources and positioning users may lead to a unsatisfactory accuracy, and positioning geometry of altitude is poor. A time division multiple address (TDMA) datalink cooperative navigation algorithm based on INS/JTIDS/BA is presented in this paper. The proposed algorithm is used to revise the errors of the inertial navigation system (INS), clock bias is calibrated via round-trip timing (RTT), and altitude is located with height filter. The TDMA datalink cooperative navigation algorithm estimate errors are stated with general navigation measurements, cooperative navigation measurements, and predicted states. Weighted horizontal geometric dilution of precision (WHDOP) of the proposed algorithm and the effect of the cooperative measurements on positioning accuracy is analyzed in theory. We simulate a joint tactical information distribution system (JTIDS) network with multiple members to evaluate the performance of the proposed algorithm. The simulation results show that compared to an extended Kalman filter (EKF) that processes TOA measurements sequentially and a TDMA datalink navigation algorithm without cooperative measurements, the TDMA datalink cooperative navigation algorithm performs better.

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Single-Satellite Integrated Navigation Algorithm Based on Broadband LEO Constellation Communication Links

Remote Sensing ◽

10.3390/rs13040703 ◽

2021 ◽

Vol 13 (4) ◽

pp. 703

Author(s):

Lvyang Ye ◽

Yikang Yang ◽

Xiaolun Jing ◽

Jiangang Ma ◽

Lingyu Deng ◽

...

Keyword(s):

Real Time ◽

Satellite Communication ◽

The State ◽

Earth Orbit ◽

Integrated Navigation ◽

Location Services ◽

The Real ◽

Navigation Algorithm ◽

Communication Links ◽

Leo Satellite

With the rapid development of satellite technology and the need to satisfy the increasing demand for location-based services, in challenging environments such as indoors, forests, and canyons, there is an urgent need to improve the position accuracy in these environments. However, traditional algorithms obtain the position solution through time redundancy in exchange for spatial redundancy, and they require continuous observations that cannot satisfy the real-time location services. In addition, they must also consider the clock bias between the satellite and receiver. Therefore, in this paper, we provide a single-satellite integrated navigation algorithm based on the elimination of clock bias for broadband low earth orbit (LEO) satellite communication links. First, we derive the principle of LEO satellite communication link clock bias elimination; then, we give the principle and process of the algorithm. Next, we model and analyze the error of the system. Subsequently, based on the unscented Kalman filter (UKF), we model the state vector and observation vector of our algorithm and give the state and observation equations. Finally, for different scenarios, we conduct qualitative and quantitative analysis through simulations, and the results show that, whether in an altimeter scenario or non-altimeter scenario, the performance indicators of our algorithm are significantly better than the inertial navigation system (INS), which can effectively overcome the divergence problem of INS; compared with the medium earth orbit (MEO) constellation, the navigation trajectory under the LEO constellation is closer to the real trajectory of the aircraft; and compared with the traditional algorithm, the accuracy of each item is improved by more than 95%. These results show that our algorithm not only significantly improves the position error, but also effectively suppresses the divergence of INS. The algorithm is more robust and can satisfy the requirements of cm-level real-time location services in challenging environments.

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A Method for Autonomous Navigation and Positioning of UAV Based on Electric Field Array Detection

Sensors ◽

10.3390/s21041146 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1146 ◽

Cited By ~ 1

Author(s):

Yincheng Li ◽

Wenbin Zhang ◽

Peng Li ◽

Youhuan Ning ◽

Chunguang Suo

Keyword(s):

Mathematical Model ◽

Power Systems ◽

Transmission Lines ◽

Autonomous Navigation ◽

Strength Distribution ◽

Field Source ◽

Navigation Algorithm ◽

Safety Warning ◽

Positioning Algorithm ◽

Navigation Equipment

At present, the method of using unmanned aerial vehicles (UAVs) with traditional navigation equipment for inspection of overhead transmission lines has the limitations of expensive sensors, difficult data processing, and vulnerable to weather and environmental factors, which cannot ensure the safety of UAV and power systems. Therefore, this paper establishes a mathematical model of spatial distribution of transmission lines to study the field strength distribution information around transmission lines. Based on this, research the navigation and positioning algorithm. The data collected by the positioning system are input into the mathematical model to complete the identification, positioning, and safety distance diagnosis of the field source. The detected data and processing results can provide reference for UAV obstacle avoidance navigation and safety warning. The experimental results show that the positioning effect of the positioning navigation algorithm is obvious, and the positioning error is within the range of use error and has good usability and application value.

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Research on outdoor AGV precise navigation based on BDS/INS data fusion

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189690 ◽

2021 ◽

pp. 1-12

Author(s):

Yongwei Tang ◽

Huijuan Hao ◽

Jun Zhou ◽

Yuexiang Lin ◽

Zhenzhen Dong

Keyword(s):

Neural Network ◽

Bp Neural Network ◽

Navigation System ◽

Integrated Navigation ◽

Complex Environments ◽

Assisted Navigation ◽

Precise Control ◽

Bee Colony ◽

Navigation Algorithm ◽

Navigation Error

AGV (Automated Guided Vehicle) technology has attracted increasing attention. Precise control of AGV position and attitude information in complex operating environment is a key part of smart factories. With outdoor AGV as a platform, this study uses BDS/INS combined navigation system combining Beidou positioning system and inertial navigation system and takes the velocity and position difference between BDS and INS as a model. An integrated navigation method is proposed to improve bee colony algorithm and optimize the BP neural network-assisted Kalman filtering to achieve accurate positioning. Moreover, the optimization of BP neural network navigation using INS navigation, network-assisted navigation and bee colony algorithm is simulated. Results demonstrate that the integrated navigation algorithm has effectiveness and feasibility, and can solve the problems of BDS misalignment and large INS navigation error in complex environments.

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