发表机构
Massachusetts Institute of Technology（麻省理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对非线性平台的规划问题，提出具有有限样本近最优性保证的DFT*算法，通过分散指令集与代价剪枝，在嵌入式处理器上实现高效实时规划，性能优于现有运动学动力学规划器。
AI 中文摘要
基于转向的规划器需要求解状态到状态的边值问题，这对于非线性平台可能无法实现。前向传播规避了转向要求，但相关规划器的有限样本行为仍未被明确，且其实现效果在实际中表现不佳。本文开发了一种基于传播的运动学动力学规划器，具有确定性的有限样本近最优性保证。我们在大类微分平坦非线性系统中开展研究，表明局部分散控制指令的前向树在经过验证的树大小下包含近最优轨迹。我们提供了一种为控制仿射系统构建分散指令集的通用机制，这是实现理论所规定的搜索算法所必需的。我们表明，无论代价如何，覆盖经过验证的轨迹类可证明需要一个随问题时域指数级增长的树，并提出了一种代价条件支配剪枝过程，该过程在树大小为时域多项式时保留近最优性。我们将得到的搜索算法——分散式前向树搜索（Dispersive Forward Tree search，DFT*）实现为前向树的广度优先扩展，其自然映射到并行硬件。我们为独轮车、拖车和四旋翼设计了高效的分散采样器，并对这些平台的挑战性规划任务进行评估。在嵌入式级处理器上，DFT*在相当的求解时间内始终提供与最先进运动学动力学规划器相当且通常显著更好的求解质量，且随着并行计算的扩展进一步加速。我们还在后退时域循环中实现DFT*，以在嵌入式级计算预算下展示动态环境中的实时规划。
英文摘要
Steering-based planners require solutions to state-to-state boundary value problems, which can be inaccessible for nonlinear platforms. Forward propagation evades the steering requirement, but the finite-sample behavior of the associated planners remains uncharacterized and their implementations underperform in practice. This paper develops a propagation-based kinodynamic planner with deterministic finite-sample near-optimality guarantees. We work within the large class of differentially flat nonlinear systems and show that a forward tree of locally dispersive control commands contains a near-optimal trajectory at a certified tree size. We provide a general mechanism to construct dispersive command sets for control-affine systems, which are necessary to implement the search algorithm prescribed by the theory. We show that covering the certified trajectory class irrespective of cost provably demands a tree exponentially sized in the problem horizon, and present a cost-conditioned dominance pruning procedure that retains near-optimality at a tree size polynomial in the horizon. We implement the resulting search algorithm, Dispersive Forward Tree search (DFT*), as breadth-first expansion of the forward tree, which maps naturally onto parallel hardware. We design efficient dispersive samplers for the unicycle, the trailer car, and the quadrotor and evaluate challenging planning tasks for these platforms. DFT* delivers consistently competitive and often substantially better solution quality than state-of-the-art kinodynamic planners at comparable solution times on embedded-tier processors, accelerating further as parallel compute is scaled. We also implement DFT* in a receding-horizon loop to demonstrate real-time planning in dynamic environments at embedded-tier compute budgets.
Comments28 pages. Code: https://github.com/croshank/DFTSearch
