# Future-Oriented Navigation: Dynamic Obstacle Avoidance with One-Shot Energy-Based Multimodal Motion Prediction

arXiv: https://arxiv.org/abs/2505.00237 · HTML: https://mlsanity.com/paper/2505.00237/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 35 references that are in the map.
- First posted on arXiv 1 May 2025.

## Builds on (most cited first, 16 of 16)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018)
- [How to Train Your Energy-Based Models](https://mlsanity.com/paper/2101.03288/) (2021)
- [From Goals, Waypoints & Paths To Long Term Human Trajectory Forecasting](https://mlsanity.com/paper/2012.01526/) (2020)
- [Core Challenges of Social Robot Navigation: A Survey](https://mlsanity.com/paper/2103.05668/) (2021)
- [Overcoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future Prediction](https://mlsanity.com/paper/1906.03631/) (2019)
- [What the Constant Velocity Model Can Teach Us About Pedestrian Motion Prediction](https://mlsanity.com/paper/1903.07933/) (2019)
- [Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph](https://mlsanity.com/paper/2203.01821/) (2022)
- [Scene Compliant Trajectory Forecast with Agent-Centric Spatio-Temporal Grids](https://mlsanity.com/paper/1909.07507/) (2019)
- [End-to-End Trajectory Distribution Prediction Based on Occupancy Grid Maps](https://mlsanity.com/paper/2203.16910/) (2022)
- [Frozone: Freezing-Free, Pedestrian-Friendly Navigation in Human Crowds](https://mlsanity.com/paper/2003.05395/) (2020)
- [Group-based Motion Prediction for Navigation in Crowded Environments](https://mlsanity.com/paper/2107.11637/) (2021)
- [Dynamic Channel: A Planning Framework for Crowd Navigation](https://mlsanity.com/paper/1903.00143/) (2019)
- [Avoiding Dense and Dynamic Obstacles in Enclosed Spaces: Application to Moving in Crowds](https://mlsanity.com/paper/2105.11743/) (2021)
- [Regulated Pure Pursuit for Robot Path Tracking](https://mlsanity.com/paper/2305.20026/) (2023)

## Abstract (arXiv)

This paper proposes an integrated approach for the safe and efficient control of mobile robots in dynamic and uncertain environments. The approach consists of two key steps: one-shot multimodal motion prediction to anticipate motions of dynamic obstacles and model predictive control to incorporate these predictions into the motion planning process. Motion prediction is driven by an energy-based neural network that generates high-resolution, multi-step predictions in a single operation. The prediction outcomes are further utilized to create geometric shapes formulated as mathematical constraints. Instead of treating each dynamic obstacle individually, predicted obstacles are grouped by proximity in an unsupervised way to improve performance and efficiency. The overall collision-free navigation is handled by model predictive control with a specific design for proactive dynamic obstacle avoidance. The proposed approach allows mobile robots to navigate effectively in dynamic environments. Its performance is accessed across various scenarios that represent typical warehouse settings. The results demonstrate that the proposed approach outperforms other existing dynamic obstacle avoidance methods.