🤖 AI Summary
This work addresses the challenge of robotic navigation in highly discontinuous and fragmented 3D environments, where disconnected pathways prevent the robot from reaching its goal. To overcome this, we extend Navigation Among Movable Objects (NAMO) to 3D settings by proposing a novel approach that strategically rearranges movable objects—such as boxes—to construct traversable bridges rather than merely removing obstacles. We introduce BRiDGE, a probabilistically complete incremental sampling-based planner that jointly explores the configuration spaces of both the robot and movable objects. BRiDGE incorporates a non-uniform sampling strategy to enhance computational efficiency and supports constraints on the number of objects to be moved. Experimental results demonstrate that our method effectively enables goal-reaching in complex, disconnected 3D environments across multiple simulated and real-world robotic platforms, with formal guarantees of probabilistic completeness.
📝 Abstract
Several planners have been developed to compute dynamically feasible, collision-free robot paths from an initial to a goal configuration. A key assumption in these works is that the goal region is reachable; an assumption that often fails in practice when environments are disconnected. Motivated by this limitation, we consider known 3D environments comprising objects, also called blocks, that form distinct navigable support surfaces (planes), and that are either non-movable (e.g., tables) or movable (e.g., boxes). These surfaces may be mutually disconnected due to height differences, holes, or lateral separations. Our focus is on tasks where the robot must reach a goal region residing on an elevated plane that is unreachable. Rather than declaring such tasks infeasible, an effective strategy is to enable the robot to interact with the environment, rearranging movable objects to create new traversable connections; a problem known as Navigation Among Movable Objects (NAMO). Existing NAMO planners typically address 2D environments, where obstacles are pushed aside to clear a path. These methods cannot directly handle the considered 3D setting; in such cases, obstacles must be placed strategically to bridge these physical disconnections. We address this challenge by developing BRiDGE (Block-based Reconfiguration in Disconnected 3D Geometric Environments), a sampling-based planner that incrementally builds trees over robot and object configurations to compute feasible plans specifying which objects to move, where to place them, and in what order, while accounting for a limited number of movable objects. To accelerate planning, we introduce non-uniform sampling strategies. We show that our method is probabilistically complete and we provide extensive numerical and hardware experiments validating its effectiveness.