We focus on multi-object rearrangement planning in densely cluttered environments using a car-like robot pusher. The combination of kinematic, geometric and physics constraints underlying this domain results in challenging nonmonotone problem instances which demand breaking each manipulation action into multiple parts to achieve a desired object rearrangement. Prior work tackles such instances by planning prerelocations, temporary object displacements that enable constraint satisfaction, but deciding where to prerelocate remains difficult due to local minima leading to infeasible or high-cost paths. Our key insight is that these minima can be avoided by steering a prerelocation optimization toward low-cost regions informed by Dubins path classification. These optimized prerelocations are integrated into an object traversability graph that encodes kinematic, geometric, and pushing constraints. Searching this graph in a depth-first fashion results in efficient, feasible rearrangement sequences. Across a series of densely cluttered scenarios with up to 13 objects, our framework, ReloPush-BOSS, exhibits consistently highest success rates and shortest pushing paths compared to state-of-the-art baselines. Hardware experiments on a 1/10 car-like pusher demonstrate the robustness of our approach. Code and footage from our experiments can be found at: https://fluentrobotics.com/relopushboss.

翻译：本文聚焦于在密集杂乱环境中使用类车机器人推杆进行多物体重排规划。该领域所涉及的动力学、几何与物理约束相结合，产生了具有挑战性的非单调问题实例，需要将每个操作动作分解为多个部分以实现期望的物体重排。先前的研究通过规划预重定位——即满足约束所需的临时物体位移——来处理此类实例，但由于局部极小值导致不可行或高成本路径，决定预重定位的位置仍然困难。我们的核心见解是，通过基于Dubins路径分类引导预重定位优化朝向低成本区域，可以避免这些极小值。这些优化后的预重定位被整合到一个编码了动力学、几何及推动约束的物体可遍历图中。以深度优先方式搜索该图，可生成高效、可行的重排序列。在一系列包含多达13个物体的密集杂乱场景中，我们的框架ReloPush-BOSS相比现有先进基线方法，始终展现出最高的成功率和最短的推动路径。在1/10比例类车推杆上的硬件实验验证了我们方法的鲁棒性。实验代码与录像可见于：https://fluentrobotics.com/relopushboss。