论文标题
HMPO：安全运动计划的封闭环境中的人类运动预测
HMPO: Human Motion Prediction in Occluded Environments for Safe Motion Planning
论文作者
论文摘要
我们提出了一种新的方法，可以在封闭环境中与人类障碍物近距离运行的机器人生成无冲突的轨迹。机器人的自我闭合可以显着降低人类运动预测的准确性，我们提出了一种新颖的基于深度学习的预测算法。我们的配方使用CNN和LSTMS，我们通过合成生成的遮挡信息来增强人为行动数据集用于培训。我们还提出了一种使用运动预测算法来计算无碰撞轨迹的遮挡意见的计划者。我们在复杂方案中强调了整体方法的性能（HMPO），并观察到运动预测准确性的绩效提高了68％，并且在地面真相与预测的人类关节位置之间的误差距离方面提高了38％。
We present a novel approach to generate collision-free trajectories for a robot operating in close proximity with a human obstacle in an occluded environment. The self-occlusions of the robot can significantly reduce the accuracy of human motion prediction, and we present a novel deep learning-based prediction algorithm. Our formulation uses CNNs and LSTMs and we augment human-action datasets with synthetically generated occlusion information for training. We also present an occlusion-aware planner that uses our motion prediction algorithm to compute collision-free trajectories. We highlight performance of the overall approach (HMPO) in complex scenarios and observe upto 68% performance improvement in motion prediction accuracy, and 38% improvement in terms of error distance between the ground-truth and the predicted human joint positions.
