Mobile Robot Navigation on Partially Known Maps using a Fast A Star Algorithm Version
This work addresses path planning for mobile robots in indoor settings, but it is incremental as it builds on existing A* variants.
The paper tackles mobile robot navigation in partially known indoor environments by evaluating an improved fast A* algorithm, showing promising results in path planning efficiency and performance with the Pioneer 2DX robot.
Mobile robot navigation in total or partially unknown environments is still an open problem. The path planning algorithms lack completeness and/or performance. Thus, there is the need for complete (i.e., the algorithm determines in finite time either a solution or correctly reports that there is none) and performance (i.e., with low computational complexity) oriented algorithms which need to perform efficiently in real scenarios. In this paper we evaluate the efficiency of two versions of the A star algorithm for mobile robot navigation inside indoor environments with the help of two software applications and the Pioneer 2DX robot. We demonstrate that an improved version of the A star algorithm (we call this the fast A star algorithm) which (a different version of this algorithm is widely used in video games) can be successfully used for indoor mobile robot navigation. We evaluated the two versions of the A star algorithm first, by implementing the algorithms in source code and by testing them on a simulator and second, by comparing two operation modes of the fast A star algorithm w.r.t. path planning efficiency (i.e., completness) and performance (i.e., time need to complete the path traversing) for indoor navigation with the Pioneer 2DX robot. The results obtained with the fast A star algorithm are promising and we think that this results can be further improved by tweaking the algorithm and by using an advanced sensor fusion approach (i.e., combine the inputs of multiple robot sensors) for better dealing with partially known environments.