# Smooth Trajectory Collision Avoidance through Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2210.06377 · HTML: https://mlsanity.com/paper/2210.06377/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 12 Oct 2022.

## Builds on (most cited first, 5 of 5)

- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Uncertainty-Aware Reinforcement Learning for Collision Avoidance](https://mlsanity.com/paper/1702.01182/) (2017)
- [Memory-based Deep Reinforcement Learning for Obstacle Avoidance in UAV with Limited Environment Knowledge](https://mlsanity.com/paper/1811.03307/) (2018)
- [Towards Monocular Vision based Obstacle Avoidance through Deep Reinforcement Learning](https://mlsanity.com/paper/1706.09829/) (2017)
- [Deep Neural Network for Real-Time Autonomous Indoor Navigation](https://mlsanity.com/paper/1511.04668/) (2015)

## Built on by (most cited first, 1 of 1)

- [Towards Deviation-Robust Agent Navigation via Perturbation-Aware Contrastive Learning](https://mlsanity.com/paper/2403.05770/) (2024)

## Abstract (arXiv)

Collision avoidance is a crucial task in vision-guided autonomous navigation. Solutions based on deep reinforcement learning (DRL) has become increasingly popular. In this work, we proposed several novel agent state and reward function designs to tackle two critical issues in DRL-based navigation solutions: 1) smoothness of the trained flight trajectories; and 2) model generalization to handle unseen environments. Formulated under a DRL framework, our model relies on margin reward and smoothness constraints to ensure UAVs fly smoothly while greatly reducing the chance of collision. The proposed smoothness reward minimizes a combination of first-order and second-order derivatives of flight trajectories, which can also drive the points to be evenly distributed, leading to stable flight speed. To enhance the agent's capability of handling new unseen environments, two practical setups are proposed to improve the invariance of both the state and reward function when deploying in different scenes. Experiments demonstrate the effectiveness of our overall design and individual components.