# Motion Primitives-based Navigation Planning using Deep Collision Prediction

arXiv: https://arxiv.org/abs/2201.03254 · HTML: https://mlsanity.com/paper/2201.03254/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2022.

## Built on by (most cited first, 11 of 11)

- [Reinforcement Learning for Collision-free Flight Exploiting Deep Collision Encoding](https://mlsanity.com/paper/2402.03947/) (2024)
- [MAVRL: Learn to Fly in Cluttered Environments with Varying Speed](https://mlsanity.com/paper/2402.08381/) (2024)
- [Task-driven Compression for Collision Encoding based on Depth Images](https://mlsanity.com/paper/2309.05289/) (2023)
- [Distance and Collision Probability Estimation from Gaussian Surface Models](https://mlsanity.com/paper/2402.00186/) (2024)
- [Competency-Aware Planning for Probabilistically Safe Navigation Under Perception Uncertainty](https://mlsanity.com/paper/2409.06111/) (2024)
- [Belief-Conditioned One-Step Diffusion: Real-Time Trajectory Planning with Just-Enough Sensing](https://mlsanity.com/paper/2508.12166/) (2025)
- [High-Speed Vision-Based Flight in Clutter with Safety-Shielded Reinforcement Learning](https://mlsanity.com/paper/2602.08653/) (2026)
- [Hierarchical Procedural Framework for Low-latency Robot-Assisted Hand-Object Interaction](https://mlsanity.com/paper/2405.19531/) (2024)
- [SafeMove-RL: A Certifiable Reinforcement Learning Framework for Dynamic Motion Constraints in Trajectory Planning](https://mlsanity.com/paper/2505.12648/) (2025)
- [CMRL: Collision-Aware and Memory-Enhanced Reinforcement Learning for UAV Navigation in Multi-Scale Obstacle Environments](https://mlsanity.com/paper/2605.14810/) (2026)
- [PILOT: Privileged Imitation Learning for End-to-End Motion Planning of Autonomous UAVs under Partial Observability](https://mlsanity.com/paper/2608.14082/) (2026)

## Abstract (arXiv)

This paper contributes a method to design a novel navigation planner exploiting a learning-based collision prediction network. The neural network is tasked to predict the collision cost of each action sequence in a predefined motion primitives library in the robot's velocity-steering angle space, given only the current depth image and the estimated linear and angular velocities of the robot. Furthermore, we account for the uncertainty of the robot's partial state by utilizing the Unscented Transform and the uncertainty of the neural network model by using Monte Carlo dropout. The uncertainty-aware collision cost is then combined with the goal direction given by a global planner in order to determine the best action sequence to execute in a receding horizon manner. To demonstrate the method, we develop a resilient small flying robot integrating lightweight sensing and computing resources. A set of simulation and experimental studies, including a field deployment, in both cluttered and perceptually-challenging environments is conducted to evaluate the quality of the prediction network and the performance of the proposed planner.