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

Predicted Composite Signed-Distance Fields for Real-Time Motion Planning in Dynamic Environments