Decremental Dynamics Planning for Robot Navigation

-- Most, if not all, robot navigation systems employ a decomposed planning framework that includes global and local planning. T o trade-off onboard computation and plan quality, current systems have to limit all robot dynamics considerations only within the local planner, while leveraging an extremely simplified robot representation (e.g., a point-mass holonomic model without dynamics) in the global level. However, such an artificial decomposition based on either full or zero consideration of robot dynamics can lead to gaps between the two levels, e.g., a global path based on a holonomic point-mass model may not be realizable by a non-holonomic robot, especially in highly constrained obstacle environments. T o validate the effectiveness of this paradigm, we augment three different planners with DDP and show overall improved planning performance. Navigation is a fundamental capability for autonomous mobile robots, enabling them to effectively traverse complex environments without collisions. As the demand for robotic systems grows across various domains, such as industrial automation, search and rescue, and autonomous delivery, the need for efficient and robust navigation strategies becomes increasingly important. Traditionally, most robot navigation systems adopt a hierarchical planning framework, decomposing the planning process into global and local planning.