Plan-Time Multi-Model Switching for Motion Planning
Plan-Time Multi-Model Switching for Motion Planning
复制标题
用于运动规划的计划时多模型切换
DOI：
10.1609/icaps.v27i1.13858
复制
发表时间：
2017
影响因子：
6.7
通讯作者：
R. Simmons
中科院分区：
文献类型：
--
作者：
Breelyn Styler;R. Simmons
机器人在非均匀环境中的导航需要可靠的运动规划生成。规划模型保真度的选择对性能有显著影响。先前的研究表明，降低模型保真度节省了规划时间，但牺牲了执行可靠性。虽然目前的自适应分层运动规划技术很有前途，但我们提出了一个框架，该框架在计划时利用了更丰富的机器人运动模型集。框架选择何时切换模型，以及哪个模型在单个轨迹中最适用。例如，更复杂的环境区域需要更高保真度的模型，而较低保真度的模型对于规划空间的简单部分就足够了，从而节省了规划时间。我们的算法一直致力于选择最能处理当前局部环境的模型。这有效地生成了一个单一的混合保真度计划。我们提出了一个具有附加拖车的模拟移动机器人在医院领域的结果。我们比较了使用单一运动规划模型和使用我们的多模型框架进行切换。我们的结果表明，多保真模型切换在不牺牲执行可靠性的情况下提高了计划时间效率。
Robot navigation through non-uniform environments requires reliable motion plan generation. The choice of planning model fidelity can significantly impact performance. Prior research has shown that reducing model fidelity saves planning time, but sacrifices execution reliability. While current adaptive hierarchical motion planning techniques are promising, we present a framework that leverages a richer set of robot motion models at plan-time. The framework chooses when to switch models and what model is most applicable within a single trajectory. For instance, more complex environment locales require higher fidelity models, while lower fidelity models are sufficient for simpler parts of the planning space, thus saving plan time. Our algorithm continuously aims to pick the model that best handles the current local environment. This effectively generates a single, mixed-fidelity plan. We present results for a simulated mobile robot with attached trailer in a hospital domain. We compare using a single motion planning model to switching with our framework of multiple models. Our results demonstrate that multi-fidelity model switching increases plan-time efficiency without sacrificing execution reliability.