机器人与场景运动学集成的序贯移动操作规划
Integration of Robot and Scene Kinematics for Sequential Mobile Manipulation Planning
AI总结：
该研究提出序贯移动操作规划（SMMP）框架，通过构建增强配置空间（A-Space）集成机器人与场景运动学，实现三级高效规划，仿真成功率提升84.6%，真实系统验证了多物体、长步骤任务的流畅执行。
摘要：
我们提出了一种序贯移动操作规划（SMMP）框架，可解决长时程多步骤移动操作任务，实现协调的全身运动，即使与铰接物体交互时也能如此。通过将环境结构抽象为运动学模型并与机器人运动学集成，我们构建了增强配置空间（A-Space），统一了之前分离的导航和操作任务约束，同时考虑了机器人基座、机械臂和被操作物体的联合可达性。这种集成促进了三级框架内的高效规划：任务规划器生成符号动作序列以建模A-Space的演化；基于优化的运动规划器在A-Space内计算连续轨迹，以实现机器人和场景元素的期望配置；中间计划细化阶段选择确保长时程可行性的动作目标。我们的仿真研究首先证实，与基线方法相比，在A-Space中规划的任务成功率高出84.6%。在真实机器人系统上的验证展示了流畅的移动操作，包括（i）17个不同场景下的7种刚性和铰接物体，以及（ii）多达14个连续步骤的长时程任务。我们的结果强调了将场景运动学建模为规划实体而非编码任务特定约束的重要性，为复杂机器人操作提供了一种可扩展且可泛化的方法。
英文摘要：
We present a Sequential Mobile Manipulation Planning (SMMP) framework that can solve long-horizon multi-step mobile manipulation tasks with coordinated whole-body motion, even when interacting with articulated objects. By abstracting environmental structures as kinematic models and integrating them with the robot's kinematics, we construct an Augmented Configuration Apace (A-Space) that unifies the previously separate task constraints for navigation and manipulation, while accounting for the joint reachability of the robot base, arm, and manipulated objects. This integration facilitates efficient planning within a tri-level framework: a task planner generates symbolic action sequences to model the evolution of A-Space, an optimization-based motion planner computes continuous trajectories within A-Space to achieve desired configurations for both the robot and scene elements, and an intermediate plan refinement stage selects action goals that ensure long-horizon feasibility. Our simulation studies first confirm that planning in A-Space achieves an 84.6\% higher task success rate compared to baseline methods. Validation on real robotic systems demonstrates fluid mobile manipulation involving (i) seven types of rigid and articulated objects across 17 distinct contexts, and (ii) long-horizon tasks of up to 14 sequential steps. Our results highlight the significance of modeling scene kinematics into planning entities, rather than encoding task-specific constraints, offering a scalable and generalizable approach to complex robotic manipulation.
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