A solution to vicinity problem of obstacles in complete coverage path planning
A solution to vicinity problem of obstacles in complete coverage path planning
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DOI：
10.1109/robot.2002.1013426
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发表时间：
2002-08
期刊：
影响因子：
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通讯作者：
C. Luo;Simon X. Yang;D. Stacey;J. Jofriet
中科院分区：
文献类型：
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作者：
C. Luo;Simon X. Yang;D. Stacey;J. Jofriet
在真实的应用中，清洁机器人的全覆盖路径规划过程中，工作空间中存在任意形状的障碍物。清洁机器人应该能够在室内环境中的各种角落和任意形状的障碍物附近进行清扫。因此，机器人不仅要有效避开障碍物，还要巧妙地覆盖障碍物附近的每个区域。针对全覆盖路径规划中的障碍物邻近问题，提出了一种基于神经邻域分析的解决方法。路径规划器是一个生物启发的神经网络。该模型能够实时规划出合理覆盖障碍物附近区域的路径。机器人路径是通过神经网络的动态神经活动景观和以前的机器人位置自主生成的。通过计算机仿真验证了该方法的有效性。
In real world applications there exist arbitrarily shaped obstacles in the workspace during complete coverage path planning of cleaning robots. A cleaning robot should be able to sweep in a variety of corners and in the vicinity of arbitrarily shaped obstacles in an indoor environment. Consequently, the robot is required not only to effectively avoid the obstacles, but also to delicately cover every area in the vicinity of obstacles. In the paper, a solution to vicinity problem of obstacles in complete coverage path planning is proposed using neural-neighborhood analysis. The path planner is a biologically inspired neural network. The proposed model is capable of planning a real-time path to reasonably cover every area in the vicinity of obstacles. The robot path is autonomously generated through the dynamic neural activity landscape of the neural network and the previous robot location. The effectiveness of the proposed approach is verified through computer simulations.