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的环境中安全地在人类周围运行。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，目前用于解决这些问题的许多最先进的算法速度太慢，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，以加速用于规划和控制的基于优化的算法的计算，从而有助于缓解这些问题。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，并且由于该项目位于一所本科女子学院，该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运行的自主系统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，该研究表明，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gpu加速混合整数求解器架构。利用领域知识和机器学习，提出了一种加速整体求解器外部分支定界层的学习并行搜索启发式算法。针对潜在的连续问题，开发了gpu加速的直接轨迹优化求解器。该底层求解器利用了轨迹优化问题的Schur补的块-三对角结构，通过一种新的对称阶梯预条件、预条件共轭梯度求解器和基于块分解的求解器。通过仿真和物理四足机器人，对整体求解器进行了评估并与最先进的方法进行了比较，以证明其在生成动态运动行为方面的有效性。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.