This paper introduces an efficient $\mathcal{O}(n)$ compute and memory complexity algorithm for globally optimal path planning on 2D Cartesian grids. Unlike existing marching methods that rely on approximate discretized solutions to the Eikonal equation, our approach achieves exact wavefront propagation by pivoting the analytic distance function based on visibility. The algorithm leverages a dynamic-programming subroutine to efficiently evaluate visibility queries. Through benchmarking against state-of-the-art any-angle path planners, we demonstrate that our method outperforms existing approaches in both speed and accuracy, particularly in cluttered environments. Notably, our method inherently provides globally optimal paths to all grid points, eliminating the need for additional gradient descent steps per path query. The same capability extends to multiple starting positions. We also provide a greedy version of our algorithm as well as open-source C++ implementation of our solver.

Pathfinding in Euclidean space is a common problem faced in robotics and computer  games. For long-distance navigation on the surface of the earth or in outer space however,  approximating the geometry as Euclidean can be insufficient for real-world applications  such as the navigation of spacecraft, aeroplanes, drones and ships. This article describes an any-angle pathfinding algorithm for calculating the shortest path between point pairs  over the surface of a sphere. Introducing several novel adaptations, it is shown that Anya  as described by Harabor & Grastien for Euclidean space can be extended to Spherical  geometry. There, where the shortest-distance line between coordinates is defined instead by a great-circle path, the optimal solution is typically a curved line in Euclidean space.  In addition the turning points for optimal paths in Spherical geometry are not necessarily  corner points as they are in Euclidean space, as will be shown, making further substantial  adaptations to Anya necessary. Spherical Anya returns the optimal path on the sphere,  given these different properties of world maps defined in Spherical geometry. It preserves all primary benefits of Anya in Euclidean geometry, namely the Spherical Anya algorithm always returns an optimal path on a sphere and does so entirely on-line, without any  preprocessing or large memory overheads. Performance benchmarks are provided for several  game maps including Starcraft and Warcraft III as well as for sea navigation on Earth  using the NOAA bathymetric dataset. Always returning the shorter path compared with  the Euclidean approximation yielded by Anya, Spherical Anya is shown to be faster than  Anya for the majority of sea routes and slower for Game Maps and Random Maps. 

Pathfinding in Euclidean space is a common problem faced in robotics and computer games. For long-distance navigation on the surface of the earth or in outer space however, approximating the geometry as Euclidean can be insufficient for real-world applications such as the navigation of spacecraft, aeroplanes, drones and ships. This article describes an any-angle pathfinding algorithm for calculating the shortest path between point pairs over the surface of a sphere. Introducing several novel adaptations, it is shown that Anya as described by (Harabor & Grastien, 2013) for Euclidean space can be extended to Spherical geometry. There, where the shortest-distance line between coordinates is defined instead by a great-circle path, the optimal solution is typically a curved line in Euclidean space. In addition the turning points for optimal paths in Spherical geometry are not necessarily corner points as they are in Euclidean space, as will be shown, making further substantial adaptations to Anya necessary. Spherical Anya returns the optimal path on the sphere, given these different properties of world maps defined in Spherical geometry. It preserves all primary benefits of Anya in Euclidean geometry, namely the Spherical Anya algorithm always returns an optimal path on a sphere and does so entirely on-line, without any preprocessing or large memory overheads. Performance benchmarks are provided for several game maps including Starcraft and Warcraft III as well as for sea navigation on Earth using the NOAA bathymetric dataset. Always returning the shorter path compared with the Euclidean approximation yielded by Anya, Spherical Anya is shown to be faster than Anya for the majority of sea routes and slower for Game Maps and Random Maps.

Hey, Have you talked with "ANYA" if not go ahead. You will find a Facebook page created by Lupin for Diabetic patients. Clicking on the message box will trigger the chatbot and accepting the terms and conditions as first chat message will start your conversation with the machine. This is the recent example of AI based product introduced in Indian Pharma sector, correct me if I'm wrong. During the test I found ANYA to be quick and responsive.However, it could have been better in terms of content.

The roles Gina Rodriguez played in "Jane the Virgin" and "Annihilation" could not be more different. In the former, Rodriguez plays a sweet and idealistic Jane Gloriana Villanueva who has strong morals and close family ties. In the latter, she plays the foul-mouthed lesbian paramedic named Anya Thorensen who has so many complex layers. Speaking to USA Today about the two roles, Rodriguez said that it felt great to be working on something so different. While she loves Jane, she cannot help but feel excited being on Anya's shoes.

In the past few years technology has completely disrupted the way organizations and companies approach customer support and care. The introduction of chatbots followed the same pattern. Lots of research exists our there to back this up, just look at this one by Net Imperative that status that 89% of customers would rather communicate with a virtual assistant than a real person. With this post, we'd like to explore chatbots in the context of using them to grow your business. We'll explore some fundamentals, imlplementation tips and even some of top tools that came up in our research.

Any-angle pathfinding is a fundamental problem in robotics and computer games. The goal is to find a shortest path between a pair of points on a grid map such that the path is not artificially constrained to the points of the grid. Prior research has focused on approximate online solutions. A number of exact methods exist but they all require super-linear space and pre-processing time. In this study, we describe Anya: a new and optimal any-angle pathfinding algorithm. Where other works find approximate any-angle paths by searching over individual points from the grid, Anya finds optimal paths by searching over sets of states represented as intervals. Each interval is identified on-the-fly. From each interval Anya selects a single representative point that it uses to compute an admissible cost estimate for the entire set. Anya always returns an optimal path if one exists. Moreover it does so without any offline pre-processing or the introduction of additional memory overheads. In a range of empirical comparisons we show that Anya is competitive with several recent (sub-optimal) online and pre-processing based techniques and is up to an order of magnitude faster than the most common benchmark algorithm, a grid-based implementation of A*.

We compare five any-angle path-planning algorithms, Theta*, Block A*, Field D*, ANYA, and Any-Angle Subgoal Graphs in terms of solution quality and runtime. Any-angle path-planning is a fairly new research area, and no direct comparison exists between these algorithms. We implement each algorithm from scratch and use similar implementations to provide a fair comparison.