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Discrete Artificial Potential Field Approach to Mobile Robot Path Planning

机译:移动机器人路径规划的离散人工潜在场方法

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The paper introduces a path planning method for an autonomous mobile robot, called the Discrete Artificial Potential Field algorithm (DAPF). The method is different from the currently applied similar path planning approaches, such as the classical APF method, using attractive and repulsive potential field functions or the wave front algorithm. The novelty and originality of the method lies in the construction of the discrete potential field, the method of taking dynamic obstacles into account and achievement of effective solutions in terms of the path length and run time of the algorithm. The DAPF algorithm is capable of finding a collision-free path for a mobile robot in static and dynamic environments. The Path Optimization Algorithm (POA) is also proposed in the paper. Its aim is to modify the collision-free path in order to obtain a smoother and shorter path. The DAPF algorithm runs in near-real time, therefore the method can be used in practical applications. The algorithm is evaluated by simulations in the MATLAB environment and by real experiments with the use of four-wheel differentially driven mobile robots. The results were compared with a heuristic approach based on Ant Colony Optimization and demonstrate the feasibility and effectiveness of the presented approach.
机译:本文介绍了一种用于自主移动机器人的路径规划方法,称为离散人工潜在场算法(DAPF)。该方法与当前应用的类似路径规划方法不同,例如经典APF方法,使用具有吸引力和排斥的潜在场功能或波前算法。该方法的新颖性和原创性在于构建离散潜在领域,在算法的路径长度和运行时间来考虑动态障碍和实现有效解决方案的方法。 DAPF算法能够在静态和动态环境中找到移动机器人的无碰撞路径。纸张中还提出了路径优化算法(POA)。它的目的是修改自由碰撞路径,以获得更光滑和更短的路径。 DAPF算法在近实时运行,因此该方法可用于实际应用。该算法通过Matlab环境中的模拟和使用四轮差动移动机器人的实验来评估。将结果与基于蚁群优化的启发式方法进行了比较,并证明了所提出的方法的可行性和有效性。

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