论文标题
预测与神经网络的样本碰撞
Predicting Sample Collision with Neural Networks
论文作者
论文摘要
许多最先进的机器人应用都需要快速有效的运动计划算法。随着机器人的维度及其工作空间的增加，尤其是碰撞检测程序的计算成本，现有的运动计划方法变得越来越有效。在这项工作中，我们提出了一个框架，以解决基于抽样的运动计划中昂贵的原始操作成本。该框架通过合同自动编码器（CAE）的新组合确定了样本机器人配置的有效性，该组合捕获了机器人工作区的占用网格的表示，以及多层的perceptron，可有效预测机器人和机器人配置的机器人的碰撞状态。我们在2D和3D工作区中使用各种机器人的多个计划问题评估了我们的框架。结果表明，（1）在所有研究的问题中，该框架在计算上是有效的，并且（2）框架对新工作区很好地推广。
Many state-of-art robotics applications require fast and efficient motion planning algorithms. Existing motion planning methods become less effective as the dimensionality of the robot and its workspace increases, especially the computational cost of collision detection routines. In this work, we present a framework to address the cost of expensive primitive operations in sampling-based motion planning. This framework determines the validity of a sample robot configuration through a novel combination of a Contractive AutoEncoder (CAE), which captures a occupancy grids representation of the robot's workspace, and a Multilayer Perceptron, which efficiently predicts the collision state of the robot from the CAE and the robot's configuration. We evaluate our framework on multiple planning problems with a variety of robots in 2D and 3D workspaces. The results show that (1) the framework is computationally efficient in all investigated problems, and (2) the framework generalizes well to new workspaces.
