GPU Robot Motion Planning Using Semi-Infinite Nonlinear Programming
GPU Robot Motion Planning Using Semi-Infinite Nonlinear Programming
复制标题
使用半无限非线性规划的 GPU 机器人运动规划
DOI：
10.1109/tpds.2016.2521373
复制
发表时间：
2016
影响因子：
5.3
通讯作者：
A. Kheddar
中科院分区：
文献类型：
--
作者：
B. Chretien;A. Escande;A. Kheddar
我们提出了一个多核GPU实现机器人运动规划制定为一个半无限优化程序。我们的方法并行计算约束及其梯度，并将结果馈送到CPU上运行的非线性优化求解器。为了确保我们的约束条件的连续满足，我们在时间间隔上使用多项式近似。由于每个约束及其梯度可以在每个时间间隔内独立计算，因此我们最终得到了一个可以利用众核架构的高度并行化问题。经典的机器人计算（几何，运动学和动力学）也可以受益于并行处理器，我们仔细研究他们的实现在我们的上下文中。这导致在GPU上运行完整的约束求值器。我们提出了几个优化的例子与人形机器人。它们揭示了在计算性能方面的实质性改进相比，并行CPU版本。
We propose a many-core GPU implementation of robotic motion planning formulated as a semi-infinite optimization program. Our approach computes the constraints and their gradients in parallel, and feeds the result to a nonlinear optimization solver running on the CPU. To ensure the continuous satisfaction of our constraints, we use polynomial approximations over time intervals. Because each constraint and its gradient can be evaluated independently for each time interval, we end up with a highly parallelizable problem that can take advantage of many-core architectures. Classic robotic computations (geometry, kinematics, and dynamics) can also benefit from parallel processors, and we carefully study their implementation in our context. This results in having a full constraint evaluator running on the GPU. We present several optimization examples with a humanoid robot. They reveal substantial improvements in terms of computation performance compared to a parallel CPU version.