Ant colony optimization algorithm for UAV path planning

Konatowski, S.; Pawłowski, Piotr

doi:10.1109/tcset.2018.8336181

Cited by 52 publications

(33 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In [73], the authors analyze how to combine swarm intelligence with multi-UAV task assignments after investigating the characteristics and principles of eleven swarm intelligence algorithms. The authors have employed PSO in [74], [75], ACO in [76], [77], GA in [1], [78] respectively to make real-time path planning for UAVs. In [79], the authors compare the parallel GA with PSO for real-time path planning and conclude that the GA produces superior trajectories compared with the PSO.…”

Section: (C) Intelligent Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

Survey on Unmanned Aerial Vehicle Networks: A Cyber Physical System Perspective

Wang

Zhao

Zhang

et al. 2020

IEEE Commun. Surv. Tutorials

186

View full text Add to dashboard Cite

Unmanned aerial vehicle (UAV) networks are playing an important role in various areas due to their agility and versatility, which have attracted significant attention from both the academia and industry in recent years. As an integration of the embedded systems with communication devices, computation capabilities and control modules, the UAV network could build a closed loop from data perceiving, information exchanging, decision making to the final execution, which tightly integrates the cyber processes into the physical devices. Therefore, the UAV network could be considered as a cyber physical system (CPS). Revealing the coupling effects among the three interacted components in this CPS system, i.e., communication, computation and control, is envisioned as the key to properly utilize all the available resources and hence improve the performance of the UAV networks. In this paper, we present a comprehensive survey on the UAV networks from a CPS perspective. Firstly, we respectively research the basics and advances with respect to the three CPS components in the UAV networks. Then we look inside to investigate how these components contribute to the system performance by classifying the UAV networks into three hierarchies, i.e., the cell level, the system level, and the system of system level. Further, the coupling effects among these CPS components are explicitly illustrated, which could be enlightening to deal with the challenges in each individual aspect. New research directions and open issues are discussed at the end of this survey. With this intensive literature review, we try to provide a novel insight into the state-of-the-art in the UAV networks.

show abstract

Section: (C) Intelligent Algorithmsmentioning

confidence: 99%

“…High performance computation platform , such as Snapdragon Flight and Jetson TX1; Path planning based on intelligent algorithms, such as PSO [74], [75], ACO [76], [77] and GA [1], [78];…”

Section: Control and Executionmentioning

confidence: 99%

Survey on Unmanned Aerial Vehicle Networks: A Cyber Physical System Perspective

Wang

Zhao

Zhang

et al. 2020

IEEE Commun. Surv. Tutorials

186

View full text Add to dashboard Cite

show abstract

“…In reference [30], the main problem is to carry out trajectory optimization by tailoring the successive convex approximation and alternating descent method to develop a joint trajectory and transmit power algorithm. Additionally, combining with path search algorithms such as the ant colony algorithm [31], genetic algorithm [32], Dijkstra [33], and A-star algorithm [34] was suggested to select the optimal trajectory without a local minimum solution. Thus, a novel method based on the CHNN can yield a better solution to the TSP.…”

Section: Related Workmentioning

confidence: 99%

Maritime Buoyage Inspection System Based on an Unmanned Aerial Vehicle and Active Disturbance Rejection Control

Sheng

et al. 2021

IEEE Access

View full text Add to dashboard Cite

The unmanned aerial vehicle (UAV) is inexpensive and offers a fast response speed and robust flexibility; thus, it is a promising tool in the maritime buoyage inspection scenario, which involves monitoring and accessing a lateral mark system far away from the coast. However, two main problems can occur during inspection. The first is extreme weather conditions, resulting in a deviation between the inspection route and the design route. The second is that the buoyage beacons are visited only once. Therefore, this article proposes a buoyage inspection system consisting of a single UAV and random coastal buoys. The UAV automatically takes off from the depot, performs a self-check on the buoy beacons, and then returns to the depot. A cascade active disturbance rejection controller (ADRC) is designed to adjust the real-time trajectory of the UAV system. A feasible trajectory planning method is also designed based on the continuous Hopfield neural network (CHNN) and genetic algorithm (GA) to minimize the inspection distance. Extensive simulations are conducted to demonstrate the effectiveness of the proposed method.

show abstract

“…In the general case, the ACO procedure [7] consists of the selection by ants of subsequent nodes from the nelement set N = {u 1 , u 2 , ..., u n } and (t) = { 1 ,  2 , ... ,  n } with the probability specified by the formula…”

Section: Aco Algorithmmentioning

confidence: 99%

“…The simulated object is the UAV platform presented as a rigid solid with six degrees of freedom 6DoF [8,10,14]. The state vector, characterizing the state of the UAV in space, is presented in the form (7) s p…”

Section: Environment and Simulation Objectmentioning

confidence: 99%

Application of the ACO algorithm for UAV path planning

Konatowski

Pawlowski

2019

ELECTROTECHNICAL REVIEW

View full text Add to dashboard Cite

The ACO (Ant Colony Optimization) algorithm is a bio-inspired metaheuristic used to optimize problems or functions described by graphs, sequences of events, or queues of tasks. It is used, among a variety of other purposes, when routing Internet network packets, determining the shortest routes between designated points (traveling salesman's problem), for the time and cost optimization of production, or setting public transport stops. In the article, the ACO algorithm was used to autonomously construct the optimal route for an unmanned aerial vehicle (UAV). The algorithm establishes the spatial orientation of the UAV, indicating the direction of its transition for each intermediate waypoint. The results of the simulations show the trajectory of the UAV depending on the selected weighting factors, determining the priority of avoiding detected hazards or choosing the shortest path. The quality of each variant is evaluated numerically by the calculated fitness function, the value of which is the sum of the costs of the transition to each intermediate route point. The effect of the algorithm is a set of executable trajectory variants, of which the one with the best fitness value is selected. Streszczenie. Algorytm ACO (ang. Ant Colony Optimization) jest bio-inspirowaną metaheurystyką, wykorzystywaną do optymalizacji problemów lub funkcji opisywanych za pomocą grafów, sekwencji zdarzeń, czy też kolejki zadań. Znajduje on zastosowanie m.in. przy trasowaniu pakietów sieci internetowych, wyznaczaniu najkrótszych tras między wyznaczonymi punktami (problem komiwojażera), optymalizacji czasu i kosztu produkcji, czy też ustalaniu przystanków transportu publicznego. W artykule, algorytm ACO został wykorzystany do autonomicznego wyznaczenia optymalnej trasy dla bezpilotowego statku powietrznego (BSP). Algorytm ustala orientację przestrzenną BSP, determinującą kierunek jego przemieszczenia dla każdego pośredniego punktu docelowego. Wyniki przeprowadzonych symulacji przedstawiają trajektorię BSP w zależności od dobranych współczynników wagowych, określających priorytet ominięcia wykrytych zagrożeń lub wybrania najkrótszej drogi. Jakość każdego wariantu jest określana liczbowo poprzez ustaloną funkcję dopasowania, której wartość stanowi suma kosztów przejścia do każdego pośredniego punktu trasy. Efektem działania algorytmu jest zbiór wykonywalnych wariantów trajektorii, z których wybrany zostaje ten o najlepszej wartości dopasowania [Zastosowaniealgorytmu ACO do wyznaczania trasy BSP]

show abstract

Ant colony optimization algorithm for UAV path planning

Cited by 52 publications

References 14 publications

Survey on Unmanned Aerial Vehicle Networks: A Cyber Physical System Perspective

Survey on Unmanned Aerial Vehicle Networks: A Cyber Physical System Perspective

Maritime Buoyage Inspection System Based on an Unmanned Aerial Vehicle and Active Disturbance Rejection Control

Application of the ACO algorithm for UAV path planning

Contact Info

Product

Resources

About