Comparison of 3D Versus 4D Path Planning for Unmanned Aerial Vehicles

2016 ◽  
Vol 66 (6) ◽  
pp. 651 ◽  
Author(s):  
Halil Cicibas ◽  
Kadir Alpaslan Demir ◽  
Nafiz Arica
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Dynamic Environments ◽  
Research Model ◽  
Time Dimension ◽  
Complex Dynamic ◽  
Aerial Vehicles ◽  
Planning Approach ◽  
Spatial Dimensions ◽  
Aerial Vehicle

<p>This research compares 3D versus 4D (three spatial dimensions and the time dimension) multi-objective and multi-criteria path-planning for unmanned aerial vehicles in complex dynamic environments. In this study, we empirically analyse the performances of 3D and 4D path planning approaches. Using the empirical data, we show that the 4D approach is superior over the 3D approach especially in complex dynamic environments. The research model consisting of flight objectives and criteria is developed based on interviews with an experienced military UAV pilot and mission planner to establish realism and relevancy in unmanned aerial vehicle flight planning. Furthermore, this study incorporates one of the most comprehensive set of criteria identified during our literature search. The simulation results clearly show that the 4D path planning approach is able to provide solutions in complex dynamic environments in which the 3D approach could not find a solution.</p>

Heterogeneous multi-unmanned aerial vehicle task planning: Simultaneous attacks on targets using the Pythagorean hodograph curve

2019 ◽  
Vol 233 (13) ◽  
pp. 4735-4749 ◽  
Author(s):  
Fei Yan ◽  
Xiaoping Zhu ◽  
Zhou Zhou ◽  
Yang Tang
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Unmanned Aerial Vehicle ◽  
Task Allocation ◽  
Planning Problem ◽  
Aerial Vehicles ◽  
Pythagorean Hodograph ◽  
Aerial Vehicle ◽  
Path Lengths ◽  
Target Path

The coupled task allocation and path planning problem for heterogeneous multiple unmanned aerial vehicles performing a search and attack mission involving obstacles and no-fly zones are addressed. The importance of the target is measured using a time-dependent value. A task allocation algorithm is proposed to obtain the maximum system utility. In the system utility function, the reward of the target, path lengths of unmanned aerial vehicles, and number of unmanned aerial vehicles to perform a simultaneous attack are considered. The path length of the unmanned aerial vehicles based on the Pythagorean hodograph curve is calculated, and it serves as the input for the task allocation problem. A resource management method for unmanned aerial vehicles is used, so that the resource consumption of the unmanned aerial vehicles can be balanced. To satisfy the requirement of simultaneous attacks and the unmanned aerial vehicle kinematic constraints in an environment involving obstacles and no-fly zones, a distributed cooperative particle swarm optimization algorithm is developed to generate flyable and safe Pythagorean hodograph curve trajectories for unmanned aerial vehicles to achieve simultaneous arrival. Monte Carlo simulations are conducted to demonstrate the performance of the proposed task allocation and path planning method.

scholarly journals A New Dynamic Path Planning Approach for Unmanned Aerial Vehicles

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Chenxi Huang ◽  
Yisha Lan ◽  
Yuchen Liu ◽  
Wen Zhou ◽  
Hongbin Pei ◽  
...  
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Artificial Potential Field ◽  
Aerial Vehicles ◽  
Dynamic Path Planning ◽  
Planning Approach ◽  
Starting Point ◽  
Moving Direction ◽  
Dynamic Path ◽  
The Cost

Dynamic path planning is one of the key procedures for unmanned aerial vehicles (UAV) to successfully fulfill the diversified missions. In this paper, we propose a new algorithm for path planning based on ant colony optimization (ACO) and artificial potential field. In the proposed algorithm, both dynamic threats and static obstacles are taken into account to generate an artificial field representing the environment for collision free path planning. To enhance the path searching efficiency, a coordinate transformation is applied to move the origin of the map to the starting point of the path and in line with the source-destination direction. Cost functions are established to represent the dynamically changing threats, and the cost value is considered as a scalar value of mobile threats which are vectors actually. In the process of searching for an optimal moving direction for UAV, the cost values of path, mobile threats, and total cost are optimized using ant optimization algorithm. The experimental results demonstrated the performance of the new proposed algorithm, which showed that a smoother planning path with the lowest cost for UAVs can be obtained through our algorithm.

A fast path planning approach for unmanned aerial vehicles

2014 ◽  
Vol 27 (13) ◽  
pp. 3446-3460 ◽  
Author(s):  
Shidong Li ◽  
Huihua Zhou ◽  
Jia Hu ◽  
Qing Ai ◽  
Chao Cai
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Aerial Vehicles ◽  
Planning Approach

scholarly journals Robust path planning for avoiding obstacles using time-environment dynamic map

2019 ◽  
Vol 53 (1-2) ◽  
pp. 214-221 ◽  
Author(s):  
Xiaolin Zhao ◽  
Yu Zhang ◽  
Boxin Zhao
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Urban Space ◽  
Field Of View ◽  
A Algorithm ◽  
Aerial Vehicles ◽  
Trace Back ◽  
Planning Approach ◽  
Dynamic Map ◽  
Feasible Path

Small unmanned aerial vehicles are widely used in urban space because of its flexibility and maneuverability. However, there are full of dynamic obstacles and immobile obstacles which will affect safe flying in urban space. In this paper, a novel integrated path planning approach for unmanned aerial vehicles is presented, which is consisted of three steps. First, a time-environment dynamic map is constructed to represent obstacles by introducing time axis. Second, unmanned aerial vehicles’ flyable paths are explored based on breadth-first algorithm. Third, a path planning method using A* algorithm and local trace-back model is designed in order to discover sub-optimal feasible path rapidly in unmanned aerial vehicles’ field of view. Finally, the simulation results have illustrated that the proposed method can ensure unmanned aerial vehicles’ autonomous path planning safely and effectively in urban space crowded with obstacles.

Cooperative Path Planning of Unmanned Aerial Vehicles

2010 ◽  
Author(s):  
Antonios Tsourdos ◽  
Brian White ◽  
Madhavan Shanmugavel
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Aerial Vehicles

scholarly journals R-DFS: A Coverage Path Planning Approach Based on Region Optimal Decomposition

2021 ◽  
Vol 13 (8) ◽  
pp. 1525
Author(s):  
Gang Tang ◽  
Congqiang Tang ◽  
Hao Zhou ◽  
Christophe Claramunt ◽  
Shaoyang Men
Keyword(s):  
Path Planning ◽  
Google Earth ◽  
Simulation Experiments ◽  
Minimum Width ◽  
Coverage Path Planning ◽  
Optimal Paths ◽  
Planning Approach ◽  
Polygonal Area ◽  
Aerial Vehicle ◽  
Working Distance

Most Coverage Path Planning (CPP) strategies based on the minimum width of a concave polygonal area are very likely to generate non-optimal paths with many turns. This paper introduces a CPP method based on a Region Optimal Decomposition (ROD) that overcomes this limitation when applied to the path planning of an Unmanned Aerial Vehicle (UAV) in a port environment. The principle of the approach is to first apply a ROD to a Google Earth image of a port and combining the resulting sub-regions by an improved Depth-First-Search (DFS) algorithm. Finally, a genetic algorithm determines the traversal order of all sub-regions. The simulation experiments show that the combination of ROD and improved DFS algorithm can reduce the number of turns by 4.34%, increase the coverage rate by more than 10%, and shorten the non-working distance by about 29.91%. Overall, the whole approach provides a sound solution for the CPP and operations of UAVs in port environments.

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