论文标题
预测在动态环境中实时运动计划的复合签名距离领域
Predicted Composite Signed-Distance Fields for Real-Time Motion Planning in Dynamic Environments
论文作者
论文摘要
我们为动态环境中的运动计划提供了一个新颖的框架，以说明场景中移动对象的预测轨迹。我们探讨了复合签名距离字段在运动计划中的使用，并详细介绍了如何实时使用它们来生成签名距离字段（SDFS），以结合预测的障碍动作。我们基于使用复合SDF的方法，以防止在工作区占用网格上执行精确的SDF计算。我们提出的技术产生的预测速度大大更快，通常显示出81--97％的时间以进行后续预测。我们将框架与GPMP2集成在一起，以实时证明我们的方法的完整实现，从而使7多道熊猫臂能平稳地避免移动的机器人。
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.
