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The path planning for an Unmanned Aerial Vehicles ensures that a dynamically feasible and collision-free path is planned between a start and an end point within a navigation environment. One of the most used algorithms for path planning is the Rapidly exploring Random Tree, where each one of its nodes is randomly collected from the navigation environment until the start and end navigation points are connected through them. The Rapidly exploring Random Tree algorithm is probabilistically complete, which ensures that a path, if one exists, will be found if the quantity of sampled nodes increases infinitely. However, there is no guarantee that the shortest path to a navigation environment will be planned by Rapidly exploring Random Tree algorithm. The Rapidly exploring Random Tree* algorithm is a path planning method that guarantees the shorter path length to the UAV but at a high computational cost. Some authors state that by informing sample collection to specific positions on the navigation environment, it would be possible to improve the planning time of this algorithm, as example of the Rapidly exploring Random Tree*-Smart algorithm, that utilize intelligent sampling and path optimization procedures to this purpose. This article introduces a novel Rapidly exploring Random Tree*-based algorithm, where a new sampling process based on Sukharev grids and convex vertices of the security hulls of obstacles is proposed. Computational tests are performed to verify that the new sampling strategy improves the planning time of Rapidly exploring Random Tree*, which can be applied to real-time navigation of Unmanned Aerial Vehicles. The results presented indicate that the use of convex vertices and grid of Sukharev accelerate the planning time of the Rapidly exploring Random Tree* and show better performance than the Rapidly exploring Random Tree*-Smart algorithm in several navigation environments with different quantities and spatial distributions of polygonal obstacles.

Path planning is one of the most important process on applications such as navigating autonomous vehicles, computer graphics, game development, robotics, and protein folding. It ensures that a path is planned between an initial and final position on the collision-free region of a search space if one exists. One of the most wide algorithms used for this purpose is the rapidly-exploring random tree (RRT), in which each node of a tree data structure is generated from a search space by a random sampling process, which originally follows a uniform spatial distribution. However, some authors claim that the addition of a non-uniform/informed approach into the sampling process of the RRT could accelerate the planning time of the algorithm. Actually, many works on literature propose different strategies to include non-uniform/informed behavior on RRT-based algorithms. However, the large number of studies on path planning subject impose difficulties on the identification of new solutions on a review process. The aim of this paper is to structure a review process to deal with the massive volume of works on this subject, by presenting the planning, development, and results of a systematic literature review (SLR), to investigate non-uniform/informed sampling solutions applied to RRT-based algorithms on path planning literature. A review protocol with two scientific questions was developed to guide the investigation. As a result, 1136 studies were selected in the path planning literature, of which 53 were identified as claiming to contain a solution with non-uniform/informed sampling on RRT-based algorithms. As a specific work is considered a scientific contribution only when it has not yet been explored in scientific circles, the results of the SLR can be used as a tool to search for what has not yet been proposed, helping to identify opportunities to contribute with new sampling processes of RRT-based algorithms. To the best knowledge of the authors, this paper presents the first development of an SLR of a topic related to the RRT algorithm. INDEX TERMS Non-uniform sampling, informed sampling, path planning, RRT, systematic literature review.

In this article, the three most used object detection approaches, Linear Binary Pattern cascade, Haarlike cascade, and Histogram of Oriented Gradients with Support Vector Machine are applied to automatic runway detection in high resolution satellite imagery and their results are compared. They have been employed predominantly for human feature recognition and this paper tests theirs applicability to runways. The results show that they can be indeed employed for this purpose with LBP and Haar approaches performing better than HOG+SVM.

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