Motion planning under sensing uncertainty is critical for robots in unstructured environments to guarantee safety for both the robot and any nearby humans. Most work on planning under uncertainty does not scale to high-dimensional robots such as manipulators, assumes simplified geometry of the robot or environment, or requires per-object knowledge of noise. Instead, we propose a method that directly models sensor-specific aleatoric uncertainty to find safe motions for high-dimensional systems in complex environments, without exact knowledge of environment geometry. We combine a novel implicit neural model of stochastic signed distance functions with a hierarchical optimization-based motion planner to plan low-risk motions without sacrificing path quality. Our method also explicitly bounds the risk of the path, offering trustworthiness. We empirically validate that our method produces safe motions and accurate risk bounds and is safer than baseline approaches.

翻译：感知不确定下的运动规划对于机器人在非结构化环境中保障自身及附近人类的安全至关重要。现有关于不确定下规划的研究大多无法扩展到机械臂等高维机器人，或假设机器人及环境具有简化几何形状，或需要逐个对象了解噪声特性。为此，我们提出一种方法，直接建模传感器特定的偶然不确定性，以在复杂环境中为高维系统寻找安全运动，而无需精确的环境几何信息。我们将新型随机符号距离函数的隐式神经模型与基于层次优化的运动规划器相结合，在不牺牲路径质量的前提下规划低风险运动。该方法还可显式限制路径风险，提供可信度保证。实验验证表明，我们的方法能生成安全运动并给出准确的风险界限，且安全性优于基线方法。