CRII: OAC: RUI: Real-Time, Mixed-Integer Model Predictive Control via Learned GPU-Acceleration

从自动驾驶汽车到机器人家庭健康辅助设备，为了让自主系统发挥其潜力，它们必须在非结构化和动态环境中围绕人类安全运行。这就要求这些系统能够快速准确地解决运动规划和控制问题。不幸的是，今天用于解决这些问题的许多最先进的算法太慢，无法实时运行，限制了此类系统。该项目通过利用并行计算和机器学习来开发新的求解器，加速用于规划和控制的基于优化的算法的计算，从而帮助缓解这些问题。该项目解决了现场机器人实际在线规划和控制的关键科学需求，并产生了可用于更广泛的科学计算领域（如运筹学）的开源求解器工件。该项目还直接投入到新的开源机器人课程的开发中，由于该项目位于一所本科女子学院，这项研究还为许多女本科生提供了参与研究的机会-其中许多是第一次。该项目解决了混合整数轨迹优化问题的计算挑战，这对于在非结构化环境中操作的自主系统中的运动规划和控制至关重要。该项目建立在最近的研究基础上，这些研究表明，这些算法可以通过使用图形处理单元（GPU）和机器学习的并行性来加速。该项目开发了一个开源的GPU加速混合整数求解器架构。通过利用领域知识和机器学习，开发了一种加速整体求解器的外部分支定界层的学习并行搜索启发式算法。一个GPU加速的直接轨迹优化求解器也开发了基本的连续问题。这个基本的求解器通过一个新的对称阶梯预处理器，预处理共轭梯度求解器和基于块分解的求解器利用Schur补的轨迹优化问题的块三对角结构。通过仿真和物理四足机器人的整体求解器进行评估和比较，以证明其在产生动态运动行为的有效性。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

From self-driving cars to robotic home-health aids, in order for autonomous systems to meet their potential, they must operate safely around humans in unstructured and dynamic environments. This requires these systems to quickly and accurately solve motion planning and control problems. Unfortunately, many state-of-the-art algorithms used to solve these problems today are too slow to run in real-time, limiting such systems. This project helps alleviate these issues by leveraging parallel computing and machine learning to develop new solvers that accelerate the computation of optimization-based algorithms used for planning and control. This project addresses critical scientific needs for practical online planning and control for field robots and results in open-source solver artifacts that can be used in wider scientific computing domains such as operations research. This project also directly feeds into the development of new open-source robotics courses and, as the project is located at an undergraduate women’s college, this research also provides opportunities for a number of women undergraduates to participate in research - many for the first time.This project addresses the computational challenges of mixed-integer trajectory optimization problems, which are crucial for motion planning and control in autonomous systems operating in unstructured environments. The project builds on recent research that has shown that these algorithms can be accelerated through parallelism using Graphics Processing Units (GPUs) and machine learning. The project develops an open-source GPU-accelerated mixed-integer solver architecture. A learned parallel search heuristic that accelerates the outer branch-and-bound layer of the overall solver is developed by leveraging domain knowledge and machine learning. A GPU-accelerated direct trajectory optimization solver is also developed for the underlying continuous problem. This underlying solver takes advantage of the block-tridiagonal structure of the Schur Complement of the trajectory optimization problem through a novel symmetric stair preconditioner, a preconditioned conjugate gradient solver, and a block-factorization-based solver. The overall solver is evaluated and compared to state-of-the-art approaches through simulation and on a physical quadruped robot to demonstrate its effectiveness in generating dynamic locomotion behaviors.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.