scholarly journals Distributed Cooperative Search Algorithm with Task Assignment and Receding Horizon Predictive Control for Multiple Unmanned Aerial Vehicles

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Kun Hou ◽  
Yajun Yang ◽  
Xuerong Yang ◽  
Jiazhe Lai
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Predictive Control ◽  
Search Algorithm ◽  
Task Assignment ◽  
Receding Horizon ◽  
Cooperative Search ◽  
Aerial Vehicles ◽  
Multiple Unmanned Aerial Vehicles

scholarly journals A Hierarchical Cooperative Mission Planning Mechanism for Multiple Unmanned Aerial Vehicles

Electronics ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 443 ◽  
Author(s):  
Zhe Zhao ◽  
Jian Yang ◽  
Yifeng Niu ◽  
Yu Zhang ◽  
Lincheng Shen
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Search Algorithm ◽  
Pseudospectral Method ◽  
Task Assignment ◽  
Mission Planning ◽  
Planning Problem ◽  
B Spline ◽  
Aerial Vehicles ◽  
Third Stage ◽  
Multiple Unmanned Aerial Vehicles

In this paper, the cooperative multi-task online mission planning for multiple Unmanned Aerial Vehicles (UAVs) is studied. Firstly, the dynamics of unmanned aerial vehicles and the mission planning problem are studied. Secondly, a hierarchical mechanism is proposed to deal with the complex multi-UAV multi-task mission planning problem. In the first stage, the flight paths of UAVs are generated by the Dubins curve and B-spline mixed method, which are defined as “CBC)” curves, where “C” stands for circular arc and “B” stands for B-spline segment. In the second stage, the task assignment problem is solved as multi-base multi-traveling salesman problem, in which the “CBC” flight paths are used to estimate the trajectory costs. In the third stage, the flight trajectories of UAVs are generated by using Gaussian pseudospectral method (GPM). Thirdly, to improve the computational efficiency, the continuous and differential initial trajectories are generated based on the “CBC” flight paths. Finally, numerical simulations are presented to demonstrate the proposed approach, the designed initial solution search algorithm is compared with existing methods. These results indicate that the proposed hierarchical mission planning method can produce satisfactory mission planning results efficiently.

scholarly journals Cooperative Search by Multiple Unmanned Aerial Vehicles in a Nonconvex Environment

2015 ◽  
Vol 2015 ◽  
pp. 1-19 ◽  
Author(s):  
Xiaoting Ji ◽  
Xiangke Wang ◽  
Yifeng Niu ◽  
Lincheng Shen
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Search Algorithm ◽  
Search Time ◽  
Transformation Method ◽  
Search Problem ◽  
Cooperative Search ◽  
Aerial Vehicles ◽  
Information Update ◽  
Multiple Unmanned Aerial Vehicles ◽  
Limited Sensing

This paper presents a distributed cooperative search algorithm for multiple unmanned aerial vehicles (UAVs) with limited sensing and communication capabilities in a nonconvex environment. The objective is to control multiple UAVs to find several unknown targets deployed in a given region, while minimizing the expected search time and avoiding obstacles. First, an asynchronous distributed cooperative search framework is proposed by integrating the information update into the coverage control scheme. And an adaptive density function is designed based on the real-time updated probability map and uncertainty map, which can balance target detection and environment exploration. Second, in order to handle nonconvex environment with arbitrary obstacles, a new transformation method is proposed to transform the cooperative search problem in the nonconvex region into an equivalent one in the convex region. Furthermore, a control strategy for cooperative search is proposed to plan feasible trajectories for UAVs under the kinematic constraints, and the convergence is proved by LaSalle’s invariance principle. Finally, by simulation results, it can be seen that our proposed algorithm is effective to handle the search problem in the nonconvex environment and efficient to find targets in shorter time compared with other algorithms.

scholarly journals Task Assignment for Multi-UAV under Severe Uncertainty by Using Stochastic Multicriteria Acceptability Analysis

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Xiaoxuan Hu ◽  
Jing Cheng ◽  
He Luo
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Task Assignment ◽  
Uncertain Information ◽  
Simulation Experiments ◽  
Aerial Vehicles ◽  
Multiple Unmanned Aerial Vehicles ◽  
Severe Uncertainty ◽  
Task Assignment Problem ◽  
Multi Uav ◽  
Assignment Approach

This paper considers a task assignment problem for multiple unmanned aerial vehicles (UAVs). The UAVs are set to perform attack tasks on a collection of ground targets in a severe uncertain environment. The UAVs have different attack capabilities and are located at different positions. Each UAV should be assigned an attack task before the mission starts. Due to uncertain information, many criteria values essential to task assignment were random or fuzzy, and the weights of criteria were not precisely known. In this study, a novel task assignment approach based on stochastic Multicriteria acceptability analysis (SMAA) method was proposed to address this problem. The uncertainties in the criteria were analyzed, and a task assignment procedure was designed. The results of simulation experiments show that the proposed approach is useful for finding a satisfactory assignment under severe uncertain circumstances.

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