Efficient Probabilistic Collision Detection for Non-Gaussian Noise Distributions
Efficient Probabilistic Collision Detection for Non-Gaussian Noise Distributions
复制标题
非高斯噪声分布的高效概率碰撞检测
DOI：
10.1109/lra.2020.2966404
复制
发表时间：
2019
影响因子：
5.2
通讯作者：
Dinesh Manocha
中科院分区：
文献类型：
--
作者：
J. S. Park;Dinesh Manocha
提出了一种有效的算法来计算位置不确定的两个物体之间的碰撞概率的严格上界，其误差分布用非高斯形式表示。我们的方法可以处理来自深度传感器的噪声数据集，其分布可能对应于截断高斯、加权样本或截断高斯混合模型。我们给出了凸形的严格概率界，并利用层次表示法将其推广到非凸形。我们强调了我们的方法在更严格的界限(10倍)和改进的运行时间(3倍)方面比以前的概率碰撞检测算法的好处。此外，我们利用我们的紧界为在传感器和运动不确定的情况下工作的七自由度机械臂设计了一种高效而准确的运动规划算法。
We present an efficient algorithm to compute tight upper bounds of collision probability between two objects with positional uncertainties, whose error distributions are represented with non-Gaussian forms. Our approach can handle noisy datasets from depth sensors, whose distributions may correspond to Truncated Gaussian, Weighted Samples, or Truncated Gaussian Mixture Model. We derive tight probability bounds for convex shapes and extend them to non-convex shapes using hierarchical representations. We highlight the benefits of our approach over prior probabilistic collision detection algorithms in terms of tighter bounds (10x) and improved running time (3x). Moreover, we use our tight bounds to design an efficient and accurate motion planning algorithm for a 7-DOF robot arm operating in tight scenarios with sensor and motion uncertainties.