基于线性转移算子的足式机器人越野导航
Off-Road Navigation of Legged Robots Using Linear Transfer Operators
AI总结：
该研究提出利用Perron-Frobenius算子将足式机器人越野导航问题转化为凸优化问题，通过地形数据实现有限维近似，相比标准迭代规划器能生成可通行性更优的路径。
摘要：
本文介绍了通过线性转移算子进行凸优化，在足式机器人上实现越野导航的方法。给定一个表征越野环境的可通行性度量，我们利用Perron-Frobenius（P-F，佩龙-弗罗贝尼乌斯）算子将导航问题提升到密度空间，从而将问题构建为凸优化形式。由于该算子作用于无限维密度空间，我们使用从地形收集的数据来获得凸优化的有限维近似。我们将越野导航的最优轨迹结果与标准迭代规划器进行了对比，结果表明，与次优的迭代规划器相比，我们的凸优化方法能为足式机器人生成可通行性更强的路径。
英文摘要：
This paper presents the implementation of off-road navigation on legged robots using convex optimization through linear transfer operators. Given a traversability measure that captures the off-road environment, we lift the navigation problem into the density space using the Perron-Frobenius (P-F) operator. This allows the problem formulation to be represented as a convex optimization. Due to the operator acting on an infinite-dimensional density space, we use data collected from the terrain to get a finite-dimension approximation of the convex optimization. Results of the optimal trajectory for off-road navigation are compared with a standard iterative planner, where we show how our convex optimization generates a more traversable path for the legged robot compared to the suboptimal iterative planner.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
