Multi-base multi-UAV cooperative reconnaissance path planning with genetic algorithm

Cao, Yan; Wei, Wanyu; Bai, Yu; Hu, Qiao

doi:10.1007/s10586-017-1132-9

Cited by 49 publications

(30 citation statements)

References 17 publications

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“…In our experiments, we initialize the restricted master problem by setting | k | = 1 for all k ∈ K, where the initial feasible route-speed combination is determined via the heuristic approach discussed in Section 3.2. We can explicitly express the pricing problem (9) in terms of the speed profiles associated with a given path P k (we drop the vehicle index for notational convenience):…”

Section: Solution Methodologymentioning

confidence: 99%

A multi‐vehicle covering tour problem with speed optimization

2021

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In the multi-vehicle covering tour problem with speed optimization, we aim to construct a set of maximal coverage routes for a fleet of vehicles that serve (observe) a set of secondary sites, given a fixed time schedule and coverage requirements. We develop an exact solution approach using a branch-and-price framework with a label-correcting algorithm and a set of innovative dominance rules to solve the resulting pricing problem. We also develop a two-stage heuristic capable of finding effective initial solutions. In addition, we consider practical extensions to the model by incorporating risk thresholds, energy capacities, and time windows. To validate our proposed solution approaches, we perform an extensive set of numerical experiments. Numerical results show the computational advantage of our proposed solution approaches compared with a state-of-the-art commercial solver.

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Section: Solution Methodologymentioning

confidence: 99%

A multi‐vehicle covering tour problem with speed optimization

2021

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“…It is inspired by Darwin's theory of natural evolution; it reflects the process of natural selection. Cao et al [29] adopted this algorithm for the problem of multi-base multi-UAV cooperative reconnaissance path planning; the problem is transformed into the shortest path combinatorial optimization using graph theory. However, the authors do not assume the occlusion caused by the terrain or obstacles.…”

Section: Literature Reviewmentioning

confidence: 99%

Collective Perception Using UAVs: Autonomous Aerial Reconnaissance in a Complex Urban Environment

Stodola

Drozd

Šilinger

et al. 2020

Sensors

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This article examines autonomous reconnaissance in a complex urban environment using unmanned aerial vehicles (UAVs). Environments with many buildings and other types of obstacles and/or an uneven terrain are harder to be explored as occlusion of objects of interest may often occur. First, in this article, the problem of autonomous reconnaissance in a complex urban environment via a swarm of UAVs is formulated. Then, the algorithm based on the metaheuristic approach is proposed for a solution. This solution lies in deploying a number of waypoints in the area of interest to be explored, from which the monitoring is performed, and planning the routes for available UAVs among these waypoints so that the monitored area is as large as possible and the operation as short as possible. In the last part of this article, two types of main experiments based on computer simulations are designed to verify the proposed algorithms. The first type focuses on comparing the results achieved on the benchmark instances with the optimal solutions. The second one presents and discusses the results obtained from a number of scenarios, which are based on typical reconnaissance operations in real environments.

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“…At present, the research methods of path planning can be divided into two categories: Global path planning and local path planning. Global path planning refers to obtaining a feasible path in a given environment through intelligent optimization algorithms or Mathematical Programming [21], In general, path planning is made for the given environment first, and then the planned route is implanted into the UAV system for off-line flight, which has low computational requirements for the UAV and relatively simple engineering application.With the rapid development of intelligent optimization algorithms, many intelligent optimization algorithms are used in path planning.Such as the Cuckoo Search algorithm(CS) [22], the Bat algorithm [23], the Grey Wolf Optimization algorithm(GWO) [24], the Genetic algorithm [25] and the Quantum Particle Swarm Optimization algorithm(QPSO) [26]. In [27], the Simulated Annealing algorithm is introduced to improve the A* algorithm for global path planning, which reduces planning time and search scope of the A* algorithm and solves the problem of long path planning time for UAV.…”

Section: Introductionmentioning

confidence: 99%

Multi-UAV Path Planning Based on Fusion of Sparrow Search Algorithm and Improved Bioinspired Neural Network

Liu¹,

Zhang

et al. 2021

IEEE Access

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Aiming at the problems of low stability of path planning, inability to avoid dynamic obstacles and long path planning for multi unmanned aerial vehicles (UAV) in mountainous environment, a path planning method for UAV was proposed based on the fusion of Sparrow Search Algorithm (SSA) and Bioinspired Neural Network (BINN). The method first scans the flight environment and smoothes the surface, then raises it to obtain the safe surface, and uses SSA to find a series of nodes with the lowest comprehensive cost on the safe surface. Then, B-spline curves are used to fit these nodes, so that the planned path is smooth to meet the flight requirements of the UAV. When the dynamic obstacle is detected in the predetermined trajectory, the improved BINN method is used to carry out local path replanning to achieve the purpose of dynamic obstacle avoidance. Computer simulation results demonstrate that the fusion algorithm can plan a collision free path in mountainous environment, and the planned path is smooth and short. Compared with Artificial Bee Colony Algorithm (ABC) and Dragonfly Algorithm (DA), the fusion algorithm has obvious advantages in stability of path planning and planned path length, and has the ability of dynamic obstacle avoidance.INDEX TERMS Path planning,multi-UAV,sparrow search algorithm,bioinspired neural network,safe surface.

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Multi-base multi-UAV cooperative reconnaissance path planning with genetic algorithm

Cited by 49 publications

References 17 publications

A multi‐vehicle covering tour problem with speed optimization

A multi‐vehicle covering tour problem with speed optimization

Collective Perception Using UAVs: Autonomous Aerial Reconnaissance in a Complex Urban Environment

Multi-UAV Path Planning Based on Fusion of Sparrow Search Algorithm and Improved Bioinspired Neural Network

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