scholarly journals Modelling and Simulation of Distributed UAV Swarm Cooperative Planning and Perception

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Haifeng Ling ◽  
Hongchuan Luo ◽  
Haisong Chen ◽  
Linyuan Bai ◽  
Tao Zhu ◽  
...  
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Real World ◽  
Simulation Platform ◽  
Real World Application ◽  
Aerial Vehicles ◽  
Uav Swarm ◽  
Different Types ◽  
Simulation Results ◽  
Cooperative Planning

As an emerging topic, the swarm of autonomous unmanned aerial vehicles (UAVs) has been attracting great attention. Due to the indeterminacy of sensors, distributed cooperative swarms have been considered to be efficient and robust but challenging to design and test. To facilitate the development of distributed swarms, it has been proposed to utilise a simulation platform for cooperative UAVs using imperfect perception. However, the existing simulation platforms cannot satisfy this demand due to a few reasons. First, they are designed for a specific purpose, and their functionalities are difficult to extend. Second, the existing platforms lack compatibility to be applied to different types of scenarios. Third, the modelling of these platforms is too simplified to simulate flight motion dynamic and noisy communication accurately, which may cause a difference in performance between the simulation and real-world application. To address the mentioned issues, this paper models the problem and proposes a simulation platform for distributed swarm cooperative perception, which addresses software engineering concerns and provides a set of extendable functionalities of a cooperative swarm, including communication, estimation, perception fusion, and path planning. The applicability of the proposed platform is verified by simulations with the real-world application. The simulation results demonstrate that the proposed system is viable.

Hurry Path Planning Based on Adaptive Genetic Algorithm

2013 ◽  
Vol 446-447 ◽  
pp. 1292-1297 ◽  
Author(s):  
Da Qiao Zhang ◽  
Jiu Fen Zhao ◽  
Gang Lei ◽  
Shun Hong Wang ◽  
Xiao Long Zheng
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Formation Flying ◽  
Planning Method ◽  
High Mode ◽  
Adaptive Genetic Algorithm ◽  
Aerial Vehicles ◽  
Simulation Results ◽  
Risk Cost

During formation flying, Unmanned Aerial Vehicles (UAV) may need to arrive at target ahead of schedule by hurry path. Given fixed flight high mode, hurry planning method was proposed based on Adaptive Genetic Algorithm (AGA), which makes the new path shorter by locally adjusting the default path. By full considering the risk of UAV flight, the hurry path got by AGA meets the requirements of the risk cost and time amount in advance. Simulation results show that the path gotten by AGA can better meet the requirements of the time amount in advance, and evade the threat area effectively too.

Three-dimensional waypoint following for fixed-wing unmanned aerial vehicles in obstacle-filled environments

2019 ◽  
Vol 234 (3) ◽  
pp. 640-654
Author(s):  
Nikhil Kumar Singh ◽  
Sikha Hota
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Optimal Path ◽  
Three Dimensional ◽  
Computation Time ◽  
Two Dimensional ◽  
Aerial Vehicles ◽  
Optimal Paths ◽  
Straight Line ◽  
Different Types ◽  
Simulation Results

The paper computes optimal paths for fixed-wing unmanned aerial vehicles with bounded turn radii to follow a series of waypoints with specified directions in a three-dimensional obstacle-filled environment. In the existing literature, it was proved that the optimal path is of circular turn–straight line–circular turn (CSC) type for two consecutive waypoint configurations, when the points are sufficiently far apart and there is no obstacle in the field. The maximum of all minimum turn radii corresponding to all possible two-dimensional circular maneuvers was used for both the initial and final turns to develop the CSC-type paths. But, this paper considers the minimum turn radii for initial and final turns, corresponding to the maneuvering planes and which produces shorter CSC-type paths. In an obstacle-filled environment the shortest path may collide with obstacles, so a strategy is proposed to switch to the next best path that does not collide with obstacles. Using this technique, a series of waypoints is followed in the presence of obstacles of different types, for example, cylindrical, hemispherical, and spherical in shapes with different sizes. Finally, simulation results are presented to show the efficiency of the algorithm for obstacle avoidance. The computation time listed here indicates the potentiality of this algorithm for implementation in real time.

scholarly journals Path Planning and Obstacle Avoidance for UAV in an Uncharted Environment

2021 ◽  
Vol 9 (8) ◽  
pp. 2353-2359
Author(s):  
Shubhankar Goje
Keyword(s):  
Operating System ◽  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Obstacle Avoidance ◽  
Obstacle Detection ◽  
Ongoing Research ◽  
Robot Operating System ◽  
Aerial Vehicles ◽  
Different Types ◽  
Infinite Loop

Abstract: The growing industry of unmanned aerial vehicles (UAV) requires an efficient and robust algorithm to decide the path of the UAV and avoid obstacles. The study of pathfinding algorithms is ongoing research not just useful in the domain of drones, but in other fields like video games (AI pathfinding), terrain traversal (mapped, unmapped, areal, underwater, land, etc.), and industries that require robots to deliver packages. This paper proposes a new pathfinding algorithm that aims to solve the problem of pathfinding in unknown 2-dimensional terrain. Based on a system of assumptions and using the help of a set of sensors aboard the UAV, the algorithm navigates the UAV from a start point to an endpoint while avoiding any shape or size of obstacles in between. To avoid multiple different types of “infinite loop” situations where the UAV gets stuck around an obstacle, a priority-based selector for intermediate destinations is created. The algorithm is found to work effectively when simulated in Gazebo on Robot Operating System (ROS). Keywords: Path Planning, UAV, Obstacle Avoidance, Drone Navigation, Obstacle Detection, Uncharted Environment.

scholarly journals Scan From the Sky: A Path Planning Method with Perception Optimization for UAVs

2021 ◽  
Author(s):  
Songhe Yuan ◽  
Kaoru Ota ◽  
Jianghai Zhao
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Optimization Method ◽  
Time Cost ◽  
Learning Models ◽  
Multiple Images ◽  
Aerial Vehicles ◽  
Simulation Results ◽  
Planning Problems

Abstract Unmanned aerial vehicles (UAVs) are frequently adopted in disaster management. The vision they provided is extremelyvaluable for rescuers. However, they face severe problems in their stability in actual disaster scenarios, as the images captured by theon-board sensors cannot consistently give enough information for deep learning models to make accurate decisions. In many cases,UAVs have to capture multiple images from different views to output final recognition results. In this paper, we desire to formulate the flypath task for UAVs, considering the actual perception needs. A new convolutional neural network (CNN) model is proposed to detectand localize the objects, such as the buildings, as well as an optimization method to find the optimal flying path to accutately recognizeas many as possible objects with a minimum time cost. The simulation results demonstrate that the proposed method is effective andefficient, and can well address the actual scene understanding and path planning problems for UAVs in the real world.

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

Optimal path planning with modified A-Star algorithm for stealth unmanned aerial vehicles in 3D network radar environment

2021 ◽  
pp. 095441002110073
Author(s):  
Zhe Zhang ◽  
Jian Wu ◽  
Jiyang Dai ◽  
Cheng He
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Optimal Path ◽  
Radar System ◽  
Planning Method ◽  
Optimal Path Planning ◽  
Aerial Vehicles ◽  
3D Network ◽  
Planning Scheme ◽  
Estimation Function

For stealth unmanned aerial vehicles (UAVs), path security and search efficiency of penetration paths are the two most important factors in performing missions. This article investigates an optimal penetration path planning method that simultaneously considers the principles of kinematics, the dynamic radar cross-section of stealth UAVs, and the network radar system. By introducing the radar threat estimation function and a 3D bidirectional sector multilayer variable step search strategy into the conventional A-Star algorithm, a modified A-Star algorithm was proposed which aims to satisfy waypoint accuracy and the algorithm searching efficiency. Next, using the proposed penetration path planning method, new waypoints were selected simultaneously which satisfy the attitude angle constraints and rank-K fusion criterion of the radar system. Furthermore, for comparative analysis of different algorithms, the conventional A-Star algorithm, bidirectional multilayer A-Star algorithm, and modified A-Star algorithm were utilized to settle the penetration path problem that UAVs experience under various threat scenarios. Finally, the simulation results indicate that the paths obtained by employing the modified algorithm have optimal path costs and higher safety in a 3D complex network radar environment, which show the effectiveness of the proposed path planning scheme.

Intelligent cooperative collision avoidance via fuzzy potential fields

Robotica ◽  
2021 ◽  
pp. 1-20
Author(s):  
Daegyun Choi ◽  
Anirudh Chhabra ◽  
Donghoon Kim
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Collision Avoidance ◽  
Fuzzy Inference ◽  
Potential Fields ◽  
Rule Base ◽  
Local Minima ◽  
Inference System ◽  
Aerial Vehicles ◽  
Different Shapes ◽  
Simulation Results

Summary This paper proposes an intelligent cooperative collision avoidance approach combining the enhanced potential field (EPF) with a fuzzy inference system (FIS) to resolve local minima and goal non-reachable with obstacles nearby issues and provide a near-optimal collision-free trajectory. A genetic algorithm is utilized to optimize parameters of membership function and rule base of the FISs. This work uses a single scenario containing all issues and interactions among unmanned aerial vehicles (UAVs) for training. For validating the performance, two scenarios containing obstacles with different shapes and several UAVs in small airspace are considered. Multiple simulation results show that the proposed approach outperforms the conventional EPF approach statistically.

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