Robust Sampling-based Motion Planning with Asymptotic Optimality Guarantees
Robust Sampling-based Motion Planning with Asymptotic Optimality Guarantees
复制标题
具有渐近最优性保证的基于鲁棒采样的运动规划
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
J. How
中科院分区：
文献类型：
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作者：
Brandon Luders;S. Karaman;J. How
本文提出了一种新颖的基于采样的规划器CC - RRT*，它能为受过程噪声、定位误差和不确定环境约束的线性高斯系统实时生成鲁棒的、渐近最优的轨迹。CC - RRT*通过使用机会约束有效地近似约束违反的风险，在每个时间步以及整个轨迹上都提供有保证的概率可行性。该算法利用RRT*的框架扩展了现有成果，为找到的成本最低的概率可行路径的渐近最优性提供了保证。一种新颖的基于风险的目标函数（在RRT*中被证明是可接受的）允许用户在最小化路径持续时间和风险规避行为之间进行权衡。这使得能够同时对软风险约束和硬概率可行性边界进行建模。仿真结果表明，CC - RRT*能够针对各种不确定性场景和动力学有效地识别出平滑、鲁棒的轨迹。
This paper presents a novel sampling-based planner, CC-RRT*, which generates robust, asymptotically optimal trajectories in real-time for linear Gaussian systems subject to process noise, localization error, and uncertain environmental constraints. CC-RRT* provides guaranteed probabilistic feasibility, both at each time step and along the entire trajectory, by using chance constraints to efficiently approximate the risk of constraint violation. This algorithm expands on existing results by utilizing the framework of RRT* to provide guarantees on asymptotic optimality of the lowest-cost probabilistically feasible path found. A novel riskbased objective function, shown to be admissible within RRT*, allows the user to trade-off between minimizing path duration and risk-averse behavior. This enables the modeling of soft risk constraints simultaneously with hard probabilistic feasibility bounds. Simulation results demonstrate that CC-RRT* can efficiently identify smooth, robust trajectories for a variety of uncertainty scenarios and dynamics.