论文标题
在配置空间中比较SONN类型以进行有效的机器人运动计划
Comparing SONN Types for Efficient Robot Motion Planning in the Configuration Space
论文作者
论文摘要
配置空间（C空间）中的运动计划会引起益处，例如平滑轨迹。随着自由度（DOF）的增加，它变得更加复杂。这是由于搜索空间和DOF的维度之间的直接关系。自组织神经网络（SONN）及其著名候选人自组织地图已被证明是减少C空间的有用工具，同时保留了其基本拓扑，如[29]中所示。在这项工作中，我们通过其他模型扩展了先前的研究，并将方法从人体运动数据转化为机器人的运动学。评估包括[29]的最佳性能模型和另外三个SONN架构，代表了先前工作的延续。在机器人模拟中成功测试了使用不同SONN模型的生成的轨迹。
Motion planning in the configuration space (C-space) induces benefits, such as smooth trajectories. It becomes more complex as the degrees of freedom (DOF) increase. This is due to the direct relation between the dimensionality of the search space and the DOF. Self-organizing neural networks (SONN) and their famous candidate, the Self-Organizing Map, have been proven to be useful tools for C-space reduction while preserving its underlying topology, as presented in [29]. In this work, we extend our previous study with additional models and adapt the approach from human motion data towards robots' kinematics. The evaluation includes the best performant models from [29] and three additional SONN architectures, representing the consequent continuation of this previous work. Generated Trajectories, planned with the different SONN models, were successfully tested in a robot simulation.
