Path planning algorithms for mobile robots are concerned with finding a feasible path between a
start and goal location in a given environment without hitting obstacles. In the existing literature, important
performance metrics for path planning algorithms are the path length, computation time and path safety,
which is quantified by the minimum distance of a path from obstacles.
The subject of this paper is the development of path planning algorithms for omni-directional robots,
which have the ability of following paths that consist of concatenated line segments. As the main contribution
of the paper, we develop three new sampling-based path planning algorithms that address all of the stated
performance metrics. The original idea of the paper is the computation of a modified environment map that
confines solution paths to the vicinity of the Voronoi boundary of the given environment. Using this modified
environment map, we adapt the sampling strategy of the popular path planning algorithms PRM (probabilistic
roadmap), PRM* and FMT (fast marching tree). As a result, we are able to generate solution paths with a
reduced computation time and increased path safety. Computational experiments with different environments
show that the proposed algorithms outperform state-of-the-art algorithms.
Primary Language | English |
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Journal Section | Articles |
Authors | |
Publication Date | November 1, 2019 |
Published in Issue | Year 2019 Volume: 16 Issue: 2 |