Chemical-Source Localization Using a Swarm of Decentralized Unmanned Aerial Vehicles for Urban/Suburban Environments

2019 ◽  
Author(s):  
Jake A. Steiner ◽  
Joseph R. Bourne ◽  
Xiang He ◽  
Donald M. Cropek ◽  
Kam K. Leang
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Source Localization ◽  
Ambient Air ◽  
Pso Algorithm ◽  
Complex Environment ◽  
Localization Method ◽  
Complex Interactions ◽  
Aerial Vehicles ◽  
Chemical Measurements ◽  
Two Phases

Abstract In this paper, a decentralized chemical-source localization method is presented. In a real-world scenario, many challenges arise, including sporadic chemical measurements due to the complex interactions between the unmanned aerial vehicles (UAVs), the ambient air, and obstacles. The localization method is split into two phases: a search phase, where the agents cover the area and look for an initial chemical reading; followed by a convergence phase, where UAV agents utilize a particle swarm optimization (PSO) algorithm to locate the source of the chemical leak. The decentralized source-localization method enables a swarm of UAVs to safely travel in a complex environment and avoid obstacles and other agents while searching for the leaking source. The method is validated in simulation using realistic dynamic chemical plumes and through outdoor flight tests using a swarm of UAVs. The results demonstrate the feasibility of the approach.

scholarly journals Mission Planning for Unmanned Aerial Vehicles Based on Voronoi Diagram-Tabu Genetic Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Wei Tan ◽  
Yong-jiang Hu ◽  
Yue-fei Zhao ◽  
Wen-guang Li ◽  
Xiao-meng Zhang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Unmanned Aerial Vehicles ◽  
Voronoi Diagram ◽  
Pso Algorithm ◽  
Mission Planning ◽  
Cooperative System ◽  
Aerial Vehicles ◽  
Rendezvous Problem ◽  
Aerial Vehicle ◽  
Multi Uav

Unmanned aerial vehicles (UAVs) are increasingly used in different military missions. In this paper, we focus on the autonomous mission allocation and planning abilities for the UAV systems. Such abilities enable adaptation to more complex and dynamic mission environments. We first examine the mission planning of a single unmanned aerial vehicle. Based on that, we then investigate the multi-UAV cooperative system under the mission background of cooperative target destruction and show that it is a many-to-one rendezvous problem. A heterogeneous UAV cooperative mission planning model is then proposed where the mission background is generated based on the Voronoi diagram. We then adopt the tabu genetic algorithm (TGA) to obtain multi-UAV mission planning. The simulation results show that the single-UAV and multi-UAV mission planning can be effectively realized by the Voronoi diagram-TGA (V-TGA). It is also shown that the proposed algorithm improves the performance by 3% in comparison with the Voronoi diagram-particle swarm optimization (V-PSO) algorithm.

scholarly journals Real-Time UAV Autonomous Localization Based on Smartphone Sensors

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4161 ◽  
Author(s):  
Boxin Zhao ◽  
Xiaolong Chen ◽  
Xiaolin Zhao ◽  
Jun Jiang ◽  
Jiahua Wei
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Real Time ◽  
Indoor Environment ◽  
Light Weight ◽  
Localization Method ◽  
Accurate Localization ◽  
Aerial Vehicles ◽  
Small Uav ◽  
Smartphone Sensors ◽  
Indoor And Outdoor

Localization in GPS-denied environments has become a bottleneck problem for small unmanned aerial vehicles (UAVs). Smartphones equipped with multi-sensors and multi-core processors provide a choice advantage for small UAVs for their high integration and light weight. However, the built-in phone sensor has low accuracy and the phone storage and computing resources are limited, which make the traditional localization methods unable to be readily converted to smartphone-based ones. The paper aims at exploring the feasibility of the phone sensors, and presenting a real-time, less memory autonomous localization method based on the phone sensors, so that the combination of “small UAV+smartphone” can operate in GPS-denied areas regardless of the overload problem. Indoor and outdoor flight experiments are carried out, respectively, based on an off-the-shelf smartphone and a XAircraft 650 quad-rotor platform. The results show that the precision performance of the phone sensors and real-time accurate localization in indoor environment is possible.

scholarly journals Distributed 3-D Path Planning for Multi-UAVs with Full Area Surveillance Based on Particle Swarm Optimization

2021 ◽  
Vol 11 (8) ◽  
pp. 3417
Author(s):  
Nafis Ahmed ◽  
Chaitali J. Pawase ◽  
KyungHi Chang
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Fitness Function ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Human Beings ◽  
Swarm Optimization ◽  
Aerial Vehicles ◽  
Area Surveillance

Collision-free distributed path planning for the swarm of unmanned aerial vehicles (UAVs) in a stochastic and dynamic environment is an emerging and challenging subject for research in the field of a communication system. Monitoring the methods and approaches for multi-UAVs with full area surveillance is needed in both military and civilian applications, in order to protect human beings and infrastructure, as well as their social security. To perform the path planning for multiple unmanned aerial vehicles, we propose a trajectory planner based on Particle Swarm Optimization (PSO) algorithm to derive a distributed full coverage optimal path planning, and a trajectory planner is developed using a dynamic fitness function. In this paper, to obtain dynamic fitness, we implemented the PSO algorithm independently in each UAV, by maximizing the fitness function and minimizing the cost function. Simulation results show that the proposed distributed path planning algorithm generates feasible optimal trajectories and update maps for the swarm of UAVs to surveil the entire area of interest.

Evolution-algorithm-based unmanned aerial vehicles path planning in complex environment

2019 ◽  
Vol 80 ◽  
pp. 106493 ◽  
Author(s):  
Xiaolei Liu ◽  
Xiaojiang Du ◽  
Xiaosong Zhang ◽  
Qingxin Zhu ◽  
Mohsen Guizani
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Complex Environment ◽  
Aerial Vehicles ◽  
Evolution Algorithm

scholarly journals Improved GNSS Localization and Byzantine Detection in UAV Swarms

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7239
Author(s):  
Shlomi Hacohen ◽  
Oded Medina ◽  
Tal Grinshpoun ◽  
Nir Shvalb
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Error Estimation ◽  
Measurement Errors ◽  
Computation Time ◽  
External Source ◽  
Localization Method ◽  
Aerial Vehicles ◽  
Byzantine Failures ◽  
Testing Techniques ◽  
Communication Traffic

Many tasks performed by swarms of unmanned aerial vehicles require localization. In many cases, the sensors that take part in the localization process suffer from inherent measurement errors. This problem is amplified when disruptions are added, either endogenously through Byzantine failures of agents within the swarm, or exogenously by some external source, such as a GNSS jammer. In this paper, we first introduce an improved localization method based on distance observation. Then, we devise schemes for detecting Byzantine agents, in scenarios of endogenous disruptions, and for detecting a disrupted area, in case the source of the problem is exogenous. Finally, we apply pool testing techniques to reduce the communication traffic and the computation time of our schemes. The optimal pool size should be chosen carefully, as very small or very large pools may impair the ability to identify the source/s of disruption. A set of simulated experiments demonstrates the effectiveness of our proposed methods, which enable reliable error estimation even amid disruptions. This work is the first, to the best of our knowledge, that embeds identification of endogenous and exogenous disruptions into the localization process.

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