In this paper, we present SeGMan, a hybrid motion planning framework that integrates sampling-based and optimization-based techniques with a guided forward search to address complex, constrained sequential manipulation challenges, such as pick-and-place puzzles. SeGMan incorporates an adaptive subgoal selection method that adjusts the granularity of subgoals, enhancing overall efficiency. Furthermore, proposed generalizable heuristics guide the forward search in a more targeted manner. Extensive evaluations in maze-like tasks populated with numerous objects and obstacles demonstrate that SeGMan is capable of generating not only consistent and computationally efficient manipulation plans but also outperform state-of-the-art approaches.

翻译：本文提出SeGMan——一种融合基于采样与基于优化的运动规划技术，并辅以引导式前向搜索的混合运动规划框架，旨在解决诸如抓放拼图等复杂受限顺序操作任务。SeGMan采用自适应子目标选择方法，可动态调整子目标的粒度，从而提升整体规划效率。此外，所提出的可泛化启发式策略能以更具针对性的方式引导前向搜索。在布满大量物体与障碍物的类迷宫任务中进行的大量评估表明，SeGMan不仅能生成稳定且计算高效的操作规划方案，其性能更超越了现有先进方法。