Semantic Trajectory Planning for Long-Distant Unmanned Aerial Vehicle Navigation in Urban Environments

Multi-unmanned aerial vehicle trajectory planning is one of the most complex global optimum problems in multi-unmanned aerial vehicle coordinated control. Results of recent research works on trajectory planning reveal persisting theoretical and practical problems. To mitigate them, this paper proposes a novel optimized artificial potential field algorithm for multi-unmanned aerial vehicle operations in a three-dimensional dynamic space. For all purposes, this study considers the unmanned aerial vehicles and obstacles as spheres and cylinders with negative electricity, respectively, while the targets are considered spheres with positive electricity. However, the conventional artificial potential field algorithm is restricted to a single unmanned aerial vehicle trajectory planning in two-dimensional space and usually fails to ensure collision avoidance. To deal with this challenge, we propose a method with a distance factor and jump strategy to resolve common problems such as unreachable targets and ensure that the unmanned aerial vehicle does not collide into the obstacles. The method takes companion unmanned aerial vehicles as the dynamic obstacles to realize collaborative trajectory planning. Besides, the method solves jitter problems using the dynamic step adjustment method and climb strategy. It is validated in quantitative test simulation models and reasonable results are generated for a three-dimensional simulated urban environment.

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Automated Geolocation in Urban Environments Using a Simple Camera-Equipped Unmanned Aerial Vehicle: A Rapid Mapping Surveying Alternative?

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9070425 ◽

2020 ◽

Vol 9 (7) ◽

pp. 425

Author(s):

Dimitrios Trigkakis ◽

George Petrakis ◽

Achilleas Tripolitsiotis ◽

Panagiotis Partsinevelos

Keyword(s):

Unmanned Aerial Vehicle ◽

Point Cloud ◽

Local Coordinate System ◽

Arbitrary Point ◽

Urban Environments ◽

Signal Interference ◽

Rapid Mapping ◽

Gnss Positioning ◽

Area Of Interest ◽

Aerial Vehicle

GNSS positioning accuracy can be degraded in areas where the surrounding object geometry and morphology interacts with the GNSS signals. Specifically, urban environments pose challenges to precise GNSS positioning because of signal interference or interruptions. Also, non-GNSS surveying methods, including total stations and laser scanners, involve time consuming practices in the field and costly equipment. The present study proposes the use of an Unmanned Aerial Vehicle (UAV) for autonomous rapid mapping that resolves the problem of localization for the drone itself by acquiring location information of characteristic points on the ground in a local coordinate system using simultaneous localization and mapping (SLAM) and vision algorithms. A common UAV equipped with a camera and at least a single known point, are enough to produce a local map of the scene and to estimate the relative coordinates of pre-defined ground points along with an additional arbitrary point cloud. The resulting point cloud is readily measurable for extracting and interpreting geometric information from the area of interest. Under two novel optimization procedures performing line and plane alignment of the UAV-camera-measured point geometries, a set of experiments determines that the localization of a visual point in distances reaching 15 m from the origin, delivered a level of accuracy under 50 cm. Thus, a simple UAV with an optical sensor and a visual marker, prove quite promising and cost-effective for rapid mapping and point localization in an unknown environment.

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Stepwise fusion algorithm with dual correction for multi-sensor navigation

International Journal of Advanced Robotic Systems ◽

10.1177/1729881418779931 ◽

2018 ◽

Vol 15 (3) ◽

pp. 172988141877993 ◽

Cited By ~ 2

Author(s):

Rong Wang ◽

Zhi Xiong ◽

Jianye Liu ◽

Yuxuan Cao

Keyword(s):

High Altitude ◽

Unmanned Aerial Vehicle ◽

Error Estimation ◽

Attitude Determination ◽

Vehicle Navigation ◽

Fusion Algorithm ◽

Aerial Vehicle ◽

Determination System ◽

Long Endurance ◽

Sensor Navigation

In high-altitude, long-endurance unmanned aerial vehicles, a celestial attitude determination system is used to enhance the inertial navigation system (INS)/global positioning system (GPS) to achieve the required attitude performance. The traditional federal filter is not applicable for INS/GPS/celestial attitude determination system information fusion because it does not consider the mutually coupled relationship between the horizontal reference error in the celestial attitude determination system and the navigation error; this limitation results in reduced navigation accuracy. This article proposes a novel stepwise fusion algorithm with dual correction for multi-sensor navigation. Considering the horizontal reference error, the celestial attitude determination system measurement model is constructed and the issues involved in applying the federal filter are discussed. Then, preliminary error estimation and horizontal reference compensation are added to the navigation architecture. In addition, a sequential update strategy is derived to estimate the attitude error with the compensated celestial attitude determination system based on the preliminary estimation. A stepwise correction filtering algorithm with interactive preliminary and sequential updates that can effectively fuse celestial attitude determination system measurements with the INS/GPS is constructed. High-altitude, long-endurance unmanned aerial vehicle navigation in a remote sensing task is simulated to verify the performance of the proposed method. The simulation results demonstrate that the horizontal reference error is effectively compensated, and the attitude accuracy is significantly improved after stepwise error estimation and correction. The proposed method also provides a novel multi-sensor integrated navigation architecture with mutually coupled errors; this architecture is beneficial in unmanned aerial vehicle navigation applications.

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