Performance Evaluation of Pathfinding Algorithms

Pathfinding is the search for an optimal path from a start location to a goal location in a given environment. In Artificial Intelligence pathfinding algorithms are typically designed as a kind of graph search. These algorithms are applicable in a wide variety of applications such as computer games, robotics, networks, and navigation systems. The performance of these algorithms is affected by several factors such as the problem size, path length, the number and distribution of obstacles, data structures and heuristics. When new pathfinding algorithms are proposed in the literature, their performance is often investigated empirically (if at all). Proper experimental design and analysis is crucial to provide an informative and non- misleading evaluation. In this research, we survey many papers and classify them according to their methodology, experimental design, and analytical techniques. We identify some weaknesses in these areas that are all too frequently found in reported approaches. We first found the pitfalls in pathfinding research and then provide solutions by creating example problems. Our research shows that spurious effects, control conditions provide solutions to avoid these pitfalls

Point seeking: a family of dynamic path finding algorithms

In the field of Artificial Intelligence, calculating the best route from one point to another, known as “path finding,” has become a common problem. If an agent cannot effectively navigate through an environment – be it real or virtual – it will often not be able to perform even the most routine tasks. For example, a Martian rover can\u27t collect samples if it can\u27t get to them; meanwhile, a computer game is not much of a challenge if your opponents can\u27t find their way around. The problem of path finding has three basic aspects: map representation, path generation, and locomotion. First, the environment must be interpreted into a form which can be processed algorithmically. Afterward, a path through this environment is planned out. A list of movement instructions or locations to travel to are then produced in order to guide the agent. During both the planning and movement of the agent, an algorithm may consider the agent\u27s limitations with regards to changes in velocity and orientation. Together, these steps serve to move an agent from its initial position to the desired location