We present a novel framework for motion planning in dynamic environments that accounts for the predicted trajectories of moving objects in the scene. We explore the use of composite signed-distance fields in motion planning and detail how they can be used to generate signed-distance fields (SDFs) in real-time to incorporate predicted obstacle motions. We benchmark our approach of using composite SDFs against performing exact SDF calculations on the workspace occupancy grid. Our proposed technique generates predictions substantially faster and typically exhibits an 81--97% reduction in time for subsequent predictions. We integrate our framework with GPMP2 to demonstrate a full implementation of our approach in real-time, enabling a 7-DoF Panda arm to smoothly avoid a moving robot.

翻译：我们为动态环境中的运动规划提出了一个新的框架,其中说明了在现场移动物体的预测轨迹。我们探索了在运动规划中使用综合签名远程字段的情况,并详细介绍了如何利用这些字段实时生成签名远程字段(SDFs)以纳入预计的障碍动议。我们把使用复合SDFs的方法与在工作空间占用网格上进行精确的SDF计算相比加以基准。我们提出的技术产生预测的速度要快得多,通常会为随后的预测减少81-97%的时间。我们把框架与GPMP2结合起来,以展示我们实时全面实施的方法,使一个7-DoF Panda臂能够顺利地避免一个移动机器人。