论文标题
使用完全卷积神经网络对多代理系统的一声路径计划
One-shot path planning for multi-agent systems using fully convolutional neural network
论文作者
论文摘要
路径规划在机器人动作执行中起着至关重要的作用，因为必须先定义针对特定动作的路径或运动轨迹，然后才能执行动作。当前的大多数方法是迭代方法，其中通过基于当前状态预测下一个状态来迭代生成轨迹。此外，在多机构系统的情况下，分别计划为每个代理计划。与此相反，我们通过利用完全卷积的神经网络提出了一种新的方法，该方法允许单次拍摄，即具有单个预测步骤，即使在多个代理中生成完整的路径。我们证明，我们的方法能够成功生成最佳或接近最佳路径，以超过98％的案例进行单路预测。此外，我们表明，尽管该网络从未在多路计划上进行过培训，但在生成两个和三个路径时，它也能够在85.7 \％和65.4％的情况下生成最佳或接近最佳路径。
Path planning plays a crucial role in robot action execution, since a path or a motion trajectory for a particular action has to be defined first before the action can be executed. Most of the current approaches are iterative methods where the trajectory is generated iteratively by predicting the next state based on the current state. Moreover, in case of multi-agent systems, paths are planned for each agent separately. In contrast to that, we propose a novel method by utilising fully convolutional neural network, which allows generation of complete paths, even for more than one agent, in one-shot, i.e., with a single prediction step. We demonstrate that our method is able to successfully generate optimal or close to optimal paths in more than 98\% of the cases for single path predictions. Moreover, we show that although the network has never been trained on multi-path planning it is also able to generate optimal or close to optimal paths in 85.7\% and 65.4\% of the cases when generating two and three paths, respectively.
