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Discrete Artificial Potential Field Approach to Mobile Robot Path Planning

机译:离散人工势场方法在移动机器人路径规划中的应用

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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,称为离散人工势场算法(DAPF)。该方法与当前应用的类似路径规划方法(例如经典APF方法)不同,该方法使用有吸引力的斥力势场函数或波前算法。该方法的新颖性和新颖性在于离散势场的构建,在算法的路径长度和运行时间方面考虑动态障碍并获得有效解决方案的方法。 DAPF算法能够为静态和动态环境中的移动机器人找到无冲突的路径。本文还提出了路径优化算法(POA)。其目的是修改无碰撞路径,以获得更平滑,更短的路径。 DAPF算法几乎实时运行,因此该方法可在实际应用中使用。通过在MATLAB环境中的仿真以及使用四轮差动驱动的移动机器人进行的实际实验对算法进行了评估。将结果与基于蚁群优化的启发式方法进行了比较,证明了该方法的可行性和有效性。

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