A mixed probabilistic–geometric strategy for UAV optimum flight path identification based on bit-coded basic manoeuvres

Blasi, Luciano; Barbato, Simeone; D’Amato, Egidio

doi:10.1016/j.ast.2017.09.007

Cited by 19 publications

(6 citation statements)

References 14 publications

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“…Algorithm. e intelligent optimization method is a model that uses biological characteristics to solve practical problems, and the PSO algorithm is a typical swarm intelligence random search optimization algorithm [21]. In PSO, the search space of the problem is similar to the flight space of a group of birds, and each bird is one of them.…”

Section: Hybrid Particle Swarm Optimizationmentioning

confidence: 99%

Virtual Reality Technology of Multi UAVEarthquake Disaster Path Optimization

Wang

Liu

2021

Mathematical Problems in Engineering

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China is a country with frequent earthquake disasters. After the occurrence of earthquake disasters, the key to disaster monitoring and rescue is to quickly obtain images of postdisaster areas. Unmanned aerial vehicle (UAV) path planning is the core of multi-UAV cooperative control. With the increasing popularity of UAVs, people with more complex living environments contact with UAVs more frequently, which also poses a challenge to the overall control of UAVs, making single UAV and even multi-UAV cooperative path planning become a hot research issue in recent years. The complexity of communication between aircraft in three-dimensional flight space and multidegree of freedom navigation makes multi-UAV cooperation more challenging. According to the research results at home and abroad, this paper takes multitarget tracking algorithm, ant colony algorithm, and hybrid particle swarm optimization algorithm as research methods. Based on virtual reality technology, by comparing the advantages and disadvantages of several algorithms, the research model of path optimization is established, and a multitarget detection method based on virtual reality technology is established. Through the analysis and improvement of multitarget tracking algorithm, ant colony algorithm, and hybrid particle swarm optimization algorithm, the path optimization problem of UAV after an earthquake based on virtual reality technology is studied. The results show that, compared with the previous research models, the overall optimization efficiency of UAV route is improved by 15%, which is more practical.

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Section: Hybrid Particle Swarm Optimizationmentioning

confidence: 99%

Virtual Reality Technology of Multi UAVEarthquake Disaster Path Optimization

Wang

Liu

2021

Mathematical Problems in Engineering

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“…• The actual response obtained from the quadrotor is compared with the desired one(φ d ,θ d and θ d ) to calculate the error and consequently, an objective function i.e. the criterion to be minimized ( 17), ( 18) or (19), is numerically evaluated,…”

Section: Metaheuristic Optimization Of Fuzzy Controllersmentioning

confidence: 99%

“…The parametric and structural optimization of a FLC makes it possible to achieve suitable performance and robustness. A multitude of fuzzy controller optimization methods have been proposed and applied in the scientific literature [17][18][19][20][21][22][23][24][25], which can be classified into two categories; the first is the optimization based on analytical methods. A prominent example of this category is the gradient descent algorithm.…”

Section: Introductionmentioning

confidence: 99%

Optimisation of fuzzy logic quadrotor attitude controller – particle swarm, cuckoo search and BAT algorithms

Zatout

Rezoug

et al. 2021

International Journal of Systems Science

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Bio-inspired optimization algorithms have recently attracted much attention in the control community. Most of these algorithms mimic particular behaviors of some animal species in such a way that allows solving optimization problems. The present paper aims at applying three metaheuristic methods for optimizing Fuzzy Logic Controllers used for quadrotor attitude stabilization. The investigated methods are Particle Swarm Optimization (PSO), BAT algorithm and Cuckoo Search (CS). These methods are applied to find the best output distribution of singleton membership functions of the Fuzzy Controllers. The quadrotor control requires measured responses, therefore, three objective functions are considered: Integral Squared Error, Integral Time-weighted Absolute Error and Integral Time-Squared Error. These metrics allow performance comparison of to compare the controllers in terms of tracking errors and speed of convergence. The simulation results indicate that BAT algorithm demonstrated higher performance than both PSO and CS. Furthermore, BAT algorithm is capable of offering 50% less computation time than CS and 10% less time than PSO. In terms of fitness, BAT algorithm achieved an average of 5% better fitness than PSO and 15% better than Cuckoo Search. According to these results, the BAT-based Fuzzy Controller exhibits superior performance compared with other algorithms to stabilize the quadrotor.

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“…An incremental path-planning algorithm for autonomous vehicles flying in an environment with moving obstacles able to take into account flight dynamics was presented in [54]. Furthermore, a trajectory planning model, coupled with a Particle Swarm Optimizer (PSO) [55] was able to identify optimal UAV flight trajectories compliant with environmental constraints. Nevertheless, these methods are computationally expensive and slow, making them only suitable for offline optimization applications.…”

Section: Introductionmentioning

confidence: 99%

Path Planning for Fixed-Wing Unmanned Aerial Vehicles: An Integrated Approach with Theta* and Clothoids

Bassolillo,

Raspaolo,

Blasi

et al. 2024

Drones

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Unmanned Aerial Vehicles (UAVs) have emerged as a compelling alternative to manned operations, offering the capability to navigate hazardous environments without risks for human operators. Despite their potential, optimizing UAV missions in complex and unstructured environments remains a pivotal challenge. Path planning becomes a crucial aspect to increase mission efficiency, although it is inherently complex due to various factors such as obstacles, no-fly zones, non-cooperative aircraft, and flight mechanics limitations. This paper presents a path-planning technique for fixed-wing unmanned aerial vehicles (UAVs) based on the Theta* algorithm. The approach introduces innovative features, such as the use of Euler spiral, or clothoids, to serve as connection arcs between nodes, mitigating trajectory discontinuities. The design of clothoids can be linked to the aircraft performance model, establishing a connection between curvature constraints and the specific characteristics of the vehicle. Furthermore, to lower the computational burden, the implementation of an adaptive exploration distance and a vision cone was considered, reducing the number of explored solutions. This methodology ensures a seamless and optimized flight path for fixed-wing UAVs operating in static environments, showcasing a noteworthy improvement in trajectory smoothness. The proposed methodology has been numerically evaluated in several complex test cases as well as in a real urban scenario to prove its effectiveness.

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A mixed probabilistic–geometric strategy for UAV optimum flight path identification based on bit-coded basic manoeuvres

Cited by 19 publications

References 14 publications

Virtual Reality Technology of Multi UAVEarthquake Disaster Path Optimization

Virtual Reality Technology of Multi UAVEarthquake Disaster Path Optimization

Optimisation of fuzzy logic quadrotor attitude controller – particle swarm, cuckoo search and BAT algorithms

Path Planning for Fixed-Wing Unmanned Aerial Vehicles: An Integrated Approach with Theta* and Clothoids

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