Publication: Any angle path finding in stochastic obstacle scenes
Abstract
Stokastik engellerin olduğu yol planlaması, çok populer bir araştırma alanıolarak bilinir. Zorlayıcı bir stokastik optimizasyon problemi olan KanadalıGezgin Probleminde (CTP), bloke edilmiş yollar içeren bir haritada engelebitişik bir köşeye ulaşıldığında önceden tanımlanmış olasılıkla bu engel anlıkolarak yok edilebilir. Stokastik engelli yol bulma probleminin (SOSP) gerçekçözümünde, sürekli bir ortamda büyük durum aralıkları gerektiren engellerbulunur. Bu nedenle, stokastik engel alanı probleminin ayrıklaştırılmışversiyonu (D-SOSP), olasılık bağımlılığı olan bir grup stokastik kenara sahipolduğu için CTP’nin en sık kullanılan çeşididir. Bu kenarların durumlarıbelirsiz, geçilebilir veya değiştirilemez olarak atanır. Amaç, engeli yok etmemaliyeti de dahil olmak üzere en kısa ayrık yolu garanti edecek bir seyahat planıtasarlamaktır. Engeli etkisizleştirme problemi (ONP), sınırlı ve önceden belirliek maliyeti olan etkisizleştirme kabiliyetini barındırır.Bu çalışmada; ayrıklaştırılmış stokastik engel alanlarında problemin tamçözümü için, önbellek kullanan AO* (CAO*) ve engeli etkisizleştiren AO*(CAON*) algoritmaları kullanarak herhangi açı (ANYA) yol bulma metodununfaydalarını sunuyoruz. Standart CAO*, kabul edilebilir üst sınırları bulurkenDijkstra’nın en kısa yol metodu kulanır. Bununla birlikte; yakın zamandaönerilen ANYA algoritmasının, ayrıklaştırılmış grafik üzerindeki üst düzey kısayol algoritmaları arasında en iyi performans gösterdiği görülmüştür. ANYA,dinamik olarak kurulan aralık kümelerini inceleyerek optimum uzunluktakiyolları arar. Gerçek hayattan bir örnek olan ABD donanma kuvvetlerine aitmayın tarlası veri haritası COBRA ve rastgele oluşturulmuş çeşitli yapayharitalar üzerinde metodolojimizi çalıştırarak elde ettiğimiz hesaplamasonuçları, belirsizliği giderme ve engeli imha etme
Path planning with stochastic obstacles is a well-known research area. The Canadiantraveler problem (CTP) is a challenging stochastic optimization problem of traversingin a given graph having blocked edges and the disambiguation status of these edgescan be settled with predefined probabilities on the fly when the agent reaches to anadjacent vertex. Stochastic obstacle scene problem (SOSP) has obstacles incontinuous environment which requires huge state spaces for the exact solution.Thus, discretized version of stochastic obstacle scene problem (D-SOSP) is mostcommonly used variant of CTP which has group of stochastic edges with probabilitydependency. States of these edges are assigned as ambiguous, traversable, oruntraversable. The objective is to design a travel plan that would guarantee theshortest path including the obstacle disambiguation cost. Obstacle neutralizationproblem (ONP) is defined as obstacle neutralization capability, which is limited andpredefined, of an agent on optimal path with an additional cost.In this work, we present Any-angle (ANYA) path finding in discretized stochasticobstacle scenes using the exact algorithms AO* with caching (CAO*) and AO* withcaching and neutralization (CAON*). The admissible upper bounds in the CAO* arefound by making use of Dijkstra’s shortest path. However, ANYA algorithm, beingrecently proposed, is already shown to outperform those shortest path algorithms atthe highest level of development on grid-based graphs. It looks for optimum lengthpaths by investigating the interval sets which are established dynamically. Ourmethodology is clearly demonstrated through computational experiments using a datamap called COBRA, a real example of US naval forces, and the use of severalsynthetically produced minefields.
Path planning with stochastic obstacles is a well-known research area. The Canadiantraveler problem (CTP) is a challenging stochastic optimization problem of traversingin a given graph having blocked edges and the disambiguation status of these edgescan be settled with predefined probabilities on the fly when the agent reaches to anadjacent vertex. Stochastic obstacle scene problem (SOSP) has obstacles incontinuous environment which requires huge state spaces for the exact solution.Thus, discretized version of stochastic obstacle scene problem (D-SOSP) is mostcommonly used variant of CTP which has group of stochastic edges with probabilitydependency. States of these edges are assigned as ambiguous, traversable, oruntraversable. The objective is to design a travel plan that would guarantee theshortest path including the obstacle disambiguation cost. Obstacle neutralizationproblem (ONP) is defined as obstacle neutralization capability, which is limited andpredefined, of an agent on optimal path with an additional cost.In this work, we present Any-angle (ANYA) path finding in discretized stochasticobstacle scenes using the exact algorithms AO* with caching (CAO*) and AO* withcaching and neutralization (CAON*). The admissible upper bounds in the CAO* arefound by making use of Dijkstra’s shortest path. However, ANYA algorithm, beingrecently proposed, is already shown to outperform those shortest path algorithms atthe highest level of development on grid-based graphs. It looks for optimum lengthpaths by investigating the interval sets which are established dynamically. Ourmethodology is clearly demonstrated through computational experiments using a datamap called COBRA, a real example of US naval forces, and the use of severalsynthetically produced minefields.