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

arXiv: https://arxiv.org/abs/2008.00969 · HTML: https://mlsanity.com/paper/2008.00969/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2020.

## Built on by (most cited first, 6 of 6)

- [iSDF: Real-Time Neural Signed Distance Fields for Robot Perception](https://mlsanity.com/paper/2204.02296/) (2022)
- [NTFields: Neural Time Fields for Physics-Informed Robot Motion Planning](https://mlsanity.com/paper/2210.00120/) (2022)
- [Regularized Deep Signed Distance Fields for Reactive Motion Generation](https://mlsanity.com/paper/2203.04739/) (2022)
- [EUGens: Efficient, Unified, and General Dense Layers](https://mlsanity.com/paper/2601.22563/) (2026)
- [EUGens: Efficient, Unified, and General Dense Layers](https://mlsanity.com/paper/2410.09771/) (2024)
- [Efficient Construction of Implicit Surface Models From a Single Image for Motion Generation](https://mlsanity.com/paper/2509.20681/) (2025)

## Abstract (arXiv)

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.