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Effects of heuristics in path planning for mobile robots in uncertain and dynamic environments

机译:不确定和动态环境中启发式方法对移动机器人路径规划的影响

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In this work, several heuristics are proposed to assess their effects in path planning for mobile robots in uncertain and dynamic environments. Path planning for mobile robots is a widely used technique where an optimal collision-free path is searched through an environment. It is one of the most important primitives in robotics and is considered to be a hard problem. Since environments may contain static as well as dynamic obstacles, the optimal path must be collision-free. The situation becomes complicated for mobile robots when an environment consists of dynamic obstacles with random velocities and arbitrary directions. Though there have been a lot of research in static environments, concentration given on uncertain and dynamic environments is a few. Our goal is to find a path in such uncertain and dynamic environments considering simulation time and travel-distance. In order to analyze the robustness of our proposed heuristics, we plugged each of these heuristics in several search algorithms. We also compared the performances of these algorithms. Finally, this paper analyzes the effects of the proposed heuristics in path planning in uncertain and dynamic environments which are usual real-word scenarios for mobile robots.
机译:在这项工作中,提出了几种启发式方法,以评估它们在不确定和动态环境中的移动机器人路径规划中的作用。移动机器人的路径规划是一种广泛使用的技术,可在环境中搜索最佳的无碰撞路径。它是机器人技术中最重要的原语之一,被认为是一个难题。由于环境可能包含静态和动态障碍物,因此最佳路径必须无碰撞。当环境由具有随机速度和任意方向的动态障碍物组成的环境时,对于移动机器人而言,情况变得更加复杂。尽管在静态环境中进行了大量研究,但集中在不确定性和动态环境上的研究却很少。我们的目标是考虑到仿真时间和行进距离,在这种不确定和动态的环境中找到一条路径。为了分析我们提出的启发式算法的鲁棒性,我们将这些启发式算法中的每一个都插入了几种搜索算法。我们还比较了这些算法的性能。最后,本文分析了所提出的启发式方法在不确定和动态环境下的路径规划中的效果,这些环境通常是移动机器人的真实单词场景。

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