论文标题
动态的通过点和改进的空间概括，用于在线轨迹计划，并具有动态运动原语
Dynamic via-points and improved spatial generalization for online trajectory planning with Dynamic Movement Primitives
论文作者
论文摘要
动态运动原语（DMP）在各种机器人任务中发现了显着的适用性和成功，这主要归因于其概括，调制和鲁棒性。然而，在某些情况下，DMP的空间概括可能是有问题的，导致过度或不自然的空间缩放。此外，没有充分解决中间点（通过点）来调整DMP轨迹。在这项工作中，我们提出了改进的在线空间概括，以补救经典DMP概括的缺点，此外，还可以纳入动态的ViaPoint。这是通过为DMP权重设计在线适应方案来实现的，该方案可将与演示的加速度剖面的距离最小化，以保留演示的形状，但要取决于动态的Via-viaint和初始/最终状态约束。进行了与经典和其他DMP变体的广泛比较模拟，而实验结果验证了所提出方法的适用性和功效。
Dynamic Movement Primitives (DMP) have found remarkable applicability and success in various robotic tasks, which can be mainly attributed to their generalization, modulation and robustness properties. Nevertheless, the spatial generalization of DMP can be problematic in some cases, leading to excessive or unnatural spatial scaling. Moreover, incorporating intermediate points (via-points) to adjust the DMP trajectory, is not adequately addressed. In this work we propose an improved online spatial generalization, that remedies the shortcomings of the classical DMP generalization, and moreover allows the incorporation of dynamic via-points. This is achieved by designing an online adaptation scheme for the DMP weights which is proved to minimize the distance from the demonstrated acceleration profile to retain the shape of the demonstration, subject to dynamic via-point and initial/final state constraints. Extensive comparative simulations with the classical and other DMP variants are conducted, while experimental results validate the applicability and efficacy of the proposed method.
