论文标题
符号计划与非线性约束解决方案之间的冲突驱动界面
A Conflict-driven Interface between Symbolic Planning and Nonlinear Constraint Solving
论文作者
论文摘要
在现实世界中，机器人计划通常需要逻辑和连续变量的联合优化。结合逻辑规划师和连续求解器的优势的核心挑战是设计有效界面的设计，该界面为逻辑上的搜索提供了有关持续不可行的搜索。在本文中，我们提出了一种新型的迭代算法，该算法通过双向界面将逻辑计划与非线性优化联系起来，这是通过检测到不可行的非线性约束的最小亚集的。该算法连续构建图形数据库，该数据库表示（在）连续变量和约束的可行子集，并在逻辑描述中编码这些知识。作为该算法的基础，我们引入了具有非线性过渡约束（PNTC）的计划，这是一种新型的计划表述，阐明了我们算法所需的确切假设，并可以将其应用于模型任务和运动计划（TAMP）。我们的实验结果表明，我们的框架明显优于tamp的替代优化方法。
Robotic planning in real-world scenarios typically requires joint optimization of logic and continuous variables. A core challenge to combine the strengths of logic planners and continuous solvers is the design of an efficient interface that informs the logical search about continuous infeasibilities. In this paper we present a novel iterative algorithm that connects logic planning with nonlinear optimization through a bidirectional interface, achieved by the detection of minimal subsets of nonlinear constraints that are infeasible. The algorithm continuously builds a database of graphs that represent (in)feasible subsets of continuous variables and constraints, and encodes this knowledge in the logical description. As a foundation for this algorithm, we introduce Planning with Nonlinear Transition Constraints (PNTC), a novel planning formulation that clarifies the exact assumptions our algorithm requires and can be applied to model Task and Motion Planning (TAMP) efficiently. Our experimental results show that our framework significantly outperforms alternative optimization-based approaches for TAMP.
