Adaptive evolutionary planner/navigator for mobile robots

Xiao, Jing; Michalewicz, Zbigniew; Zhang, Lixin; Trojanowski, Krzysztof

doi:10.1109/4235.585889

Cited by 333 publications

(15 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A heuristics-based planner was presented in [7,8] for local trajectory planning in partially known environments with moving obstacles and predefined global paths. Popup threats during UAV navigation were considered in [9,10]. A method was also proposed to provide a smooth collision-free path with appropriate control commands [11].…”

Section: Nomenclaturementioning

confidence: 99%

Unmanned Aerial Vehicles Evolutional Flight Route Planner Using the Potential Field Approach

Lin

Lee

Huang

et al. 2012

Journal of Aerospace Computing, Information, and Communication

View full text Add to dashboard Cite

This study proposes a new flight route-planning technique for autonomous navigation of unmanned aerial vehicles (UAVs) based on the combination of evolutionary algorithms with virtual potential fields. By combining a radial force field with a swirling force field, three-dimensional virtual potential fields are constructed for repelling infeasible UAV flight routes from threatening zones. To ensure feasibility, major flight constraints are considered when searching for the optimal flight route. This study examines both single-and multiple-obstacle cases to determine the efficiency of the proposed flight route planner. The UAV navigation method uses an offline planner in known environments and an online planner for flight route replanning when popup threats emerge. Both planners were tested under various scenarios. The results show that the proposed planner can efficiently enable the safe navigation of UAVs. NomenclatureCroute k = objective term in a neural network system d k;i;j = distance of ith waypoint of kth flight route to jth obstacle froute k = constrained objective function in a neural network system F max = parameter used to modulate the potential field within R j and S j F Total k;i = resultant force influencing ith node of kth flight route F Multi k;i;j = virtual potential force F xy , F z = horizontal and virtual potential forces L max , L min = maximal and minimal lengths of route segment and amplitude of oscillation O xyz = virtual potential field O w i = swirling repulsive force o Multi j = coordinates of overall center mass Proute k = penalty term in a neural network system p n = end node; goal position p 0 = starting position p 1 = second node q f k;i = correction for ith node in kth chromosome according to magnitudes of virtual force R j = minimal radius measured from o j , which covers entire obstacle S j = sphere of influence (radial extent of force from o j ) s k;i = vector of ith segment in cylinder diameter of kth chromosome T 0 xyz = virtual force field of threat areas near dangerous zone v xy , v z = horizontal and vertical velocities x dir g , y dir g , z dir g = expected direction at goal position x dir s , y dir s , z dir s = direction of unmanned aerial vehicle at starting position max = maximal climbing angle of unmanned aerial vehicle max = maximal diving angles of unmanned aerial vehicle max = maximal turning angle of unmanned aerial vehicle x , y , z = direction of magnitude of mutation on corresponding axis

show abstract

Section: Nomenclaturementioning

confidence: 99%

Unmanned Aerial Vehicles Evolutional Flight Route Planner Using the Potential Field Approach

Lin

Lee

Huang

et al. 2012

Journal of Aerospace Computing, Information, and Communication

View full text Add to dashboard Cite

show abstract

“…A repair algorithm is developed in GA algorithms for solving numerical optimization problems with nonlinear constraints where two separate populations are kept, one for marking "search points" which are to be repaired and evaluated, and the other for keeping "reference points" which are used for evaluation directly [47]. A repair algorithm for robot path planning problems is presented through designed genetic operators based on prior knowledge [48]. A genetic repair operator is designed in parallel GA algorithms for the traveling salesman problem and the graph partitioning problem by constructing a new feasible chromosome after identifying all gene loci and alleles [49].…”

Section: Related Workmentioning

confidence: 99%

New meta-heuristic for dynamic scheduling in permutation flowshop with new order arrival

Liu

Jin

Price

2018

Int J Adv Manuf Technol

View full text Add to dashboard Cite

Permutation flowshop scheduling problem (PFSP) has attracted lots of attention from both academia and industry as it finds many applications especially for today's mass customization. The PFSP is proved NP-hard, and the dynamic uncertainties such as stochastic new order arrivals significantly increase the problem complexity and difficulty. Many enterprises often struggle to make decisions on accepting new orders and setting due dates for them due to lack of effective scheduling methods. To fill in the knowledge gap, this paper is to propose a new meta-heuristic algorithm which is based on a new enhanced destruction and construction method and a novel repair method while adopting the architecture of the iterated greedy algorithm. Statistical tests were conducted and results show that the new algorithm outperforms existing ones. The maximum completion time of the new order

show abstract

“…Genetik Algoritma (GA); çok boyutlu, geniş ve karmaşık bir arama alanına sahip, matematiksel bir modelle ifade edilemeyen, kesin bir çözüm yöntemi bulunmayan durumlarda etkili ve kullanışlı olan stokastik bir optimizasyon yöntemi olarak öne çıkmaktadır [18]. Rastgele sayılarla oluşturulan başlangıç popülasyonu [19], sıralı çaprazlama [20], iki noktalı çaprazlama [21], tek noktalı çaprazlama [19,22], bitişik çaprazlama [23], sezgisel çaprazlama [24], yeni genetik operatörler [19], araya ekleme mutasyonu [20], tek tip mutasyon [22], tek tip olan ve olmayan mutasyon operatörlerinin bir arada kullanılması [24]; GA'ların genel çerçevede aynı olmalarına rağmen küçük değişikliklerle çok farklı sonuçlar verebileceğinin örnekleridir.…”

Section: Giriş (Introduction)unclassified

Parçali Hücresel Geneti̇k Algori̇tma İle İnsansiz Hava Araci Performansina Dayali Yol Planlama

Gezer,

Turan,

Baklacıoğlu

2024

GUMMFD

View full text Add to dashboard Cite

İnsansız Hava Araçları (İHA), endüstriyel, askerî ve ticari geniş bir uygulama alanına sahiptir. Değişken amaçlar için tasarlanmış farklı yeteneklere ve boyutlara sahip İHA’ların; planlama, yönetme ve koordinasyonunu sağlayabilmek için hatasız çalışan kapsamlı alt sistemlere ihtiyaç vardır. İHA teknolojik gelişiminin önemli bir parçası, yol planlama alanındaki iyileştirmelerden oluşmaktadır. Yol planlamada operasyonel önceliklere göre farklı tercihler yapılabilir, varış noktasına en hızlı şekilde ulaşılması veya hızdan ödün vererek havada kalma süresinin uzatılması istenebilir. Bir İHA’ya ait uçabildiği her hız ve her irtifa için; seyir, tırmanma ve alçalma fazlarına ait yakıt verileri yol planlama algoritmasında kullanılmıştır. Böylece, bir İHA için özelleştirilmiş kinematik kısıtlara uyumlu performans özellikleri temelinde ekonomik ve havada kalma süresini uzatan yollar üretilebilmiştir. Bu tez çalışmasında, Hücresel (cGA) ve Parçalı Hücresel Genetik Algoritma (scGA) önerilmiştir. Sabit başlangıç popülasyonu ve parçalı kromozom yapısına sahip aşırı korumacı yeni algoritma; optimal çözüme yüksek yakınsama hızı elde etmiş, geleneksel bir genetik algoritmaya (GA) kıyasla ortalama 5,2 kat daha yüksek uygunluk değerine sahip yollar üretebilmiştir. scGA’nın GA’ya kıyasla, başlangıç popülasyonuna göre en iyi çözümü 1,9 kat ve genel popülasyonu 5,8 kat daha iyi geliştirdiği gözlenmiştir.

show abstract

Adaptive evolutionary planner/navigator for mobile robots

Cited by 333 publications

References 21 publications

Unmanned Aerial Vehicles Evolutional Flight Route Planner Using the Potential Field Approach

Unmanned Aerial Vehicles Evolutional Flight Route Planner Using the Potential Field Approach

New meta-heuristic for dynamic scheduling in permutation flowshop with new order arrival

Parçali Hücresel Geneti̇k Algori̇tma İle İnsansiz Hava Araci Performansina Dayali Yol Planlama

Contact Info

Product

Resources

About