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.

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.

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.

Detection and localization of helipad in autonomous UAV landing: a coupled visual-inertial approach with artificial intelligence

2020 ◽  
Vol 71 (7) ◽  
pp. 828-839
Author(s):  
Thinh Hoang Dinh ◽  
Hieu Le Thi Hong
Keyword(s):  
Neural Network ◽  
Unmanned Aerial Vehicles ◽  
Fiducial Marker ◽  
Localization Algorithm ◽  
Challenging Problem ◽  
Autonomous Landing ◽  
Aerial Vehicles ◽  
Set Up ◽  
Detection And Localization ◽  
Rotary Wing

Autonomous landing of rotary wing type unmanned aerial vehicles is a challenging problem and key to autonomous aerial fleet operation. We propose a method for localizing the UAV around the helipad, that is to estimate the relative position of the helipad with respect to the UAV. This data is highly desirable to design controllers that have robust and consistent control characteristics and can find applications in search – rescue operations. AI-based neural network is set up for helipad detection, followed by optimization by the localization algorithm. The performance of this approach is compared against fiducial marker approach, demonstrating good consensus between two estimations

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.

Collaborative Scheduling of Algorithms for Path Planning of Unmanned Systems

2021 ◽  
Vol 01 ◽  
Author(s):  
Ying Li ◽  
Chubing Guo ◽  
Jianshe Wu ◽  
Xin Zhang ◽  
Jian Gao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Reinforcement Learning ◽  
Path Planning ◽  
Unmanned Systems ◽  
Ant Colony Optimization Algorithm ◽  
A Algorithm ◽  
Collaborative Scheduling ◽  
Simulation Results ◽  
Planning Problems ◽  
Effective Path

Background: Unmanned systems have been widely used in multiple fields. Many algorithms have been proposed to solve path planning problems. Each algorithm has its advantages and defects and cannot adapt to all kinds of requirements. An appropriate path planning method is needed for various applications. Objective: To select an appropriate algorithm fastly in a given application. This could be helpful for improving the efficiency of path planning for Unmanned systems. Methods: This paper proposes to represent and quantify the features of algorithms based on the physical indicators of results. At the same time, an algorithmic collaborative scheme is developed to search the appropriate algorithm according to the requirement of the application. As an illustration of the scheme, four algorithms, including the A-star (A*) algorithm, reinforcement learning, genetic algorithm, and ant colony optimization algorithm, are implemented in the representation of their features. Results: In different simulations, the algorithmic collaborative scheme can select an appropriate algorithm in a given application based on the representation of algorithms. And the algorithm could plan a feasible and effective path. Conclusion: An algorithmic collaborative scheme is proposed, which is based on the representation of algorithms and requirement of the application. The simulation results prove the feasibility of the scheme and the representation of algorithms.

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