JPS Algorithm Adaptation and Optimization to Three-dimensional Space

The aim of this thesis is to research the principles of the Jump Point Search (JPS) pathfinding algorithm and study the possibilities of adapting JPS to three-dimensional environment. JPS is partly based on A* algorithm but its performance is significantly better than the original A* algorithm. At the moment, there is no known 3D pathfinding algorithm published which uses the same principles as JPS. The motivation of this work is to find out what changes will be needed on the algorithm so that it will work in 3D and improve performance of the 3D version. Special target is performance comparison to the original A* algorithm and 3D JPS algorithm on inside a layered 3D space (building). Pathfinding inside building is common pathfinding problem in a video games as well it has importance also in real life. The algorithms were implemented by using Unity 3D game engine with C# language. For the purpose of research, A* and JPS algorithms were tested in the same 3D test environment. This arrangement made it possible to run the algorithms with the same test data to get a meaningful performance comparison between the algorithms. As a result, this gives also the exact principles how to adapt JPS Algorithm on a 3D environment. Moreover, it provides a novel idea and practical implementation on how to optimize the JPS 3D algorithm to get an improved performance. The presented results also include performance measurements for comparing JPS 3D method and its optimized variant in different environments

A comprehensive study on pathfinding techniques for robotics and video games

This survey provides an overview of popular pathfinding algorithms and techniques based on graph generation problems. We focus on recent developments and improvements in existing techniques and examine their impact on robotics and the video games industry. We have categorized pathfinding algorithms based on a 2D/3D environment search. The aim of this paper is to provide researchers with a thorough background on the progress made in the last 10 years in this field, summarize the principal techniques, and describe their results. We also give our expectations for future trends in this field and discuss the possibility of using pathfinding techniques in more extensive areas

Rectangle expansion A∗ pathfinding for grid maps

AbstractSearch speed, quality of resulting paths and the cost of pre-processing are the principle evaluation metrics of a pathfinding algorithm. In this paper, a new algorithm for grid-based maps, rectangle expansion A∗ (REA∗), is presented that improves the performance of A∗ significantly. REA∗ explores maps in units of unblocked rectangles. All unnecessary points inside the rectangles are pruned and boundaries of the rectangles (instead of individual points within those boundaries) are used as search nodes. This makes the algorithm plot fewer points and have a much shorter open list than A∗. REA∗ returns jump and grid-optimal path points, but since the line of sight between jump points is protected by the unblocked rectangles, the resulting path of REA∗ is usually better than grid-optimal. The algorithm is entirely online and requires no offline pre-processing. Experimental results for typical benchmark problem sets show that REA∗ can speed up a highly optimized A∗ by an order of magnitude and more while preserving completeness and optimality. This new algorithm is competitive with other highly successful variants of A∗

Reducing Redundant Work in Jump Point Search

JPS (Jump Point Search) is a state-of-the-art optimal algorithm for online grid-based pathfinding. Widely used in games and other navigation scenarios, JPS nevertheless can exhibit pathological behaviours which are not well studied: (i) it may repeatedly scan the same area of the map to find successors; (ii) it may generate and expand suboptimal search nodes. In this work, we examine the source of these pathological behaviours, show how they can occur in practice, and propose a purely online approach, called Constrained JPS (CJPS), to tackle them efficiently. Experimental results show that CJPS has low overheads and is often faster than JPS in dynamically changing grid environments: by up to 7x in large game maps and up to 14x in pathological scenarios

Unaprijeđeni algoritam za praćenje putanje na neravnoj cesti

The path planning problem is an important problem in the research area of robot, games and group animation. This paper shows a 2.5-dimensional terrain grid which can reduce the amount of computation. By applying the fuzzy logic theory, the terrain trafficability of the rugged road can be evaluated based on different gradient, roughness, elevation difference; the trafficability factor can be achieved and applied to the heuristic function. The improved algorithm can solve the symmetry problem of path planning on uneven surfaces, reduce the search space.Problem planirana putanje je važan problem u istraživačkom području robotike, igara i grupne animacije. U ovom radu teren je predstavljen 2.5-dimenzionalnom mrežom što može smanjiti vrijeme računanja. Korištenjem teorije neizrazite logike prohodnost neravne ceste može se procijeniti na osnovu razlike gradijenata, nagiba i grbavosti, te se može odrediti faktor prohodnosti koji je primijenjiv na heurističku funkciju. Unaprijeđeni algoritam može riješiti problem simetrije kod planiranja putanje na neravnim površinama i smanjiti prostor pretraživanja

Path Planning with Modified a Star Algorithm for a Mobile Robot

AbstractThis article deals with path planning of a mobile robot based on a grid map. Essential assumption for path planning is a mobile robot with functional and reliable reactive navigation and SLAM. Therefore, such issues are not addressed in this article. The main body of the article introduces several modifications (Basic Theta*, Phi*) and improvements (RSR, JPS) of A star algorithm. These modifications are focused primarily on computational time and the path optimality. Individual modifications were evaluated in several scenarios, which varied in the complexity of environment. On the basis of these evaluations, it is possible to choose path planning method suitable for individual scenario

Real-Time Planning with Multi-Fidelity Models for Agile Flights in Unknown Environments

Autonomous navigation through unknown environments is a challenging task that entails real-time localization, perception, planning, and control. UAVs with this capability have begun to emerge in the literature with advances in lightweight sensing and computing. Although the planning methodologies vary from platform to platform, many algorithms adopt a hierarchical planning architecture where a slow, low-fidelity global planner guides a fast, high-fidelity local planner. However, in unknown environments, this approach can lead to erratic or unstable behavior due to the interaction between the global planner, whose solution is changing constantly, and the local planner; a consequence of not capturing higher-order dynamics in the global plan. This work proposes a planning framework in which multi-fidelity models are used to reduce the discrepancy between the local and global planner. Our approach uses high-, medium-, and low-fidelity models to compose a path that captures higher-order dynamics while remaining computationally tractable. In addition, we address the interaction between a fast planner and a slower mapper by considering the sensor data not yet fused into the map during the collision check. This novel mapping and planning framework for agile flights is validated in simulation and hardware experiments, showing replanning times of 5-40 ms in cluttered environments.Comment: ICRA 201