arxiv.org·2026/10/26
为什么重要 为非线性/黑箱（含学习型）动力学系统提供带约束保证的规划工具，具身系统规划栈可参考。
我们提出轨迹束图方法，用于具有非线性与黑箱动力学的系统的运动规划，将基于采样的运动规划、轨迹优化与图搜索相结合。基于工作空间的凸集图分解，GTB 迭代地构建一个轨迹束图，在每个顶点处刻画系统的局部动力学。
通过在该图上求解最短路径问题，我们找到最大程度动力学可行的迭代解：这些解在极限意义下满足系统动力学，并且凭借底层图的构造，满足碰撞规避以及额外的安全或规约约束等任意约束。
因此，GTB 能够处理高度非线性、不稳定甚至黑箱的动力学，并应对复杂的碰撞与规约约束，成为复杂系统运动规划的强大工具。
We present the Graphs of Trajectory Bundles (GTB) method for motion planning of systems with nonlinear and black-box dynamics, combining sampling-based motion planning with trajectory optimization and graph search.
Based on a graph-of-convex-sets decomposition of the workspace, GTB iteratively constructs a graph of trajectory bundles that captures the local dynamics of the system at any vertex.
By solving a shortest-path problem on this graph, we find maximally dynamically feasible iterates that satisfy the system's dynamics in the limit and any constraints such as collision avoidance or additional safety or specification constraints by construction of the underlying graph.
As a result, GTB can handle highly nonlinear, unstable, and even black-box dynamics with complex collision and specification constraints, making it a powerful tool for motion planning of complex systems.
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