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.

中文翻译：

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用神经网络预测样本碰撞

许多最先进的机器人应用需要快速高效的运动规划算法。随着机器人及其工作空间的维数增加，尤其是碰撞检测例程的计算成本，现有的运动规划方法变得不那么有效。在这项工作中，我们提出了一个框架来解决基于采样的运动规划中昂贵的原始操作的成本。该框架通过一个新颖的组合来确定样本机器人配置的有效性，它通过一个新颖的组合来捕获机器人工作空间的占用网格表示的收缩自动编码器 (CAE) 和一个多层感知器，它从 CAE 有效地预测机器人的碰撞状态和机器人的配置。我们在 2D 和 3D 工作空间中使用各种机器人评估我们的框架，以解决多个规划问题。结果表明：（1）该框架在所有研究的问题上都具有计算效率，（2）该框架可以很好地推广到新的工作空间。