Understanding Safety for Unmanned Aerial Vehicles in Urban Environments

2021 ◽  
Author(s):  
Tabea Schmidt ◽  
Florian Hauer ◽  
Alexander Pretschner
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Urban Environments ◽  
Aerial Vehicles

Detection of unmanned aerial vehicles (UAV) in urban environments

2018 ◽  
Author(s):  
Dirk Nuessler ◽  
Alex Shoykhetbrod ◽  
Christian Krebs ◽  
Sabine Gütgemann ◽  
Nils Pohl ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Urban Environments ◽  
Aerial Vehicles

scholarly journals Bird Velocity Optimization as Inspiration for Unmanned Aerial Vehicles in Urban Environments

AIAA Journal ◽  
2021 ◽  
pp. 1-14
Author(s):  
Cara J. Williamson ◽  
Anouk Spelt ◽  
Shane P. Windsor
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Urban Environments ◽  
Aerial Vehicles

scholarly journals Autonomous Exploration of Urban Environments using Unmanned Aerial Vehicles

2014 ◽  
Vol 31 (6) ◽  
pp. 912-939 ◽  
Author(s):  
Benjamin Adler ◽  
Junhao Xiao ◽  
Jianwei Zhang
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Urban Environments ◽  
Autonomous Exploration ◽  
Aerial Vehicles

Tracking Multiple Ground Targets in Urban Environments Using Cooperating Unmanned Aerial Vehicles

2015 ◽  
Vol 137 (5) ◽  
Author(s):  
Vitaly Shaferman ◽  
Tal Shima
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Optimization Problem ◽  
Task Assignment ◽  
Monte Carlo Study ◽  
Urban Environments ◽  
Test Bed ◽  
Team Members ◽  
Distributed Approach ◽  
Aerial Vehicles ◽  
Cooperative Tracking

A distributed approach is proposed for planning a cooperative tracking task for a team of heterogeneous unmanned aerial vehicles (UAVs) tracking multiple predictable ground targets in a known urban environment. The solution methodology involves finding visibility regions, from which the UAV can maintain line-of-sight to each target during the scenario, and restricted regions, in which a UAV cannot fly, due to the presence of buildings or other airspace limitations. These regions are then used to pose a combined task assignment and motion planning optimization problem, in which each UAV's cost function is associated with its location relative to the visibility and restricted regions, and the tracking performance of the other UAVs in the team. A distributed co-evolution genetic algorithm (CEGA) is derived for solving the optimization problem. The proposed solution is scalable, robust, and computationally parsimonious. The algorithm is centralized, implementing a distributed computation approach; thus, global information is used and the computational workload is divided between the team members. This enables the execution of the algorithm in relatively large teams of UAVs servicing a large number of targets. The viability of the algorithm is demonstrated in a Monte Carlo study, using a high fidelity simulation test-bed incorporating a visual database of an actual city.

scholarly journals An Innovative Algorithm to Estimate Risk Optimum Path for Unmanned Aerial Vehicles in Urban Environments

2018 ◽  
Vol 35 ◽  
pp. 44-53 ◽  
Author(s):  
Stefano Primatesta ◽  
Luca Spanò Cuomo ◽  
Giorgio Guglieri ◽  
Alessandro Rizzo
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Urban Environments ◽  
Aerial Vehicles ◽  
Optimum Path

scholarly journals Complex Environment Path Planning for Unmanned Aerial Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5250
Author(s):  
Jing Zhang ◽  
Jiwu Li ◽  
Hongwei Yang ◽  
Xin Feng ◽  
Geng Sun
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Urban Environments ◽  
Planning Problem ◽  
Pruning Algorithm ◽  
Aerial Vehicles ◽  
Global Path Planning ◽  
Narrow Passage ◽  
Local Path ◽  
Path Planning Problem

Flying safely in complex urban environments is a challenge for unmanned aerial vehicles because path planning in urban environments with many narrow passages and few dynamic flight obstacles is difficult. The path planning problem is decomposed into global path planning and local path adjustment in this paper. First, a branch-selected rapidly-exploring random tree (BS-RRT) algorithm is proposed to solve the global path planning problem in environments with narrow passages. A cyclic pruning algorithm is proposed to shorten the length of the planned path. Second, the GM(1,1) model is improved with optimized background value named RMGM(1,1) to predict the flight path of dynamic obstacles. Herein, the local path adjustment is made by analyzing the prediction results. BS-RRT demonstrated a faster convergence speed and higher stability in narrow passage environments when compared with RRT, RRT-Connect, P-RRT, 1-0 Bg-RRT, and RRT*. In addition, the path planned by BS-RRT through the use of the cyclic pruning algorithm was the shortest. The prediction error of RMGM(1,1) was compared with those of ECGM(1,1), PCGM(1,1), GM(1,1), MGM(1,1), and GDF. The trajectory predicted by RMGM(1,1) was closer to the actual trajectory. Finally, we use the two methods to realize path planning in urban environments.

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