Using previous experience for humanoid navigation planning
Using previous experience for humanoid navigation planning
复制标题
利用以往的经验进行人形导航规划
DOI：
10.1109/humanoids.2016.7803364
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
D. Berenson
中科院分区：
文献类型：
--
作者：
Yu;D. Berenson
我们提出了一个人形机器人导航规划框架，重用以前的经验，以减少规划时间。该框架旨在使用手掌和足部接触来导航复杂的非结构化环境。在复杂环境中，基于离散搜索的接触空间规划器在高分支因子和动作灵活性之间进行权衡。虽然加权A*、ARA* 和ANA* 等方法可以通过牺牲最优性来加快搜索速度，但当启发式不准确时，它们可能非常慢。在建议的框架中，经验检索模块被添加到并行ANA*。该模块收集先前生成的运动计划，并基于接触姿势相似性将它们聚类以形成运动计划库。为了从库中检索用于给定环境的适当计划，框架使用计划和环境表面中的接触姿势之间的距离。实验结果表明，该框架在非结构化环境中的成功率至少比从头开始的规划方法高28%，并且可以在碎石和狭窄的走廊等困难环境中导航。
We propose a humanoid robot navigation planning framework that reuses previous experience to decrease planning time. The framework is intended for navigating complex unstructured environments using both palm and foot contacts. In a complex environment, discrete-search-based contact space planners trade-off between high branching factor and action flexibility. Although approaches such as weighted A*, ARA* and ANA* could speed up the search by compromising on optimality, they can be very slow when the heuristic is inaccurate. In the proposed framework, an experience-retrieval module is added in parallel to ANA*. This module collects previously-generated motion plans and clusters them based on contact pose similarity to form a motion plan library. To retrieve an appropriate plan from the library for a given environment, the framework uses a distance between the contact poses in the plan and environment surfaces. Candidate plans are then modified with local trajectory optimization until a plan fitting the query environment is found. Our experiments show that the proposed framework outperforms planning-from-scratch in success rate in unstructured environments by at least 28% and can navigate difficult environments such as rubble and narrow corridors.