# Monocular Obstacle Avoidance Based on Inverse PPO for Fixed-wing UAVs

arXiv: https://arxiv.org/abs/2411.18009 · HTML: https://mlsanity.com/paper/2411.18009/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 19 of its 50 references that are in the map.
- First posted on arXiv 27 Nov 2024.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 19 of 19)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth](https://mlsanity.com/paper/2302.12288/) (2023)
- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [Rewrite the Stars](https://mlsanity.com/paper/2403.19967/) (2024)
- [Understanding Multi-Step Deep Reinforcement Learning: A Systematic Study of the DQN Target](https://mlsanity.com/paper/1901.07510/) (2019)
- [Temporal-Logic-Based Reward Shaping for Continuing Reinforcement Learning Tasks](https://mlsanity.com/paper/2007.01498/) (2020)
- [Towards Monocular Vision based Obstacle Avoidance through Deep Reinforcement Learning](https://mlsanity.com/paper/1706.09829/) (2017)
- [R'enyi State Entropy for Exploration Acceleration in Reinforcement Learning](https://mlsanity.com/paper/2203.04297/) (2022)
- [Nonlinear MPC for Collision Avoidance and Controlof UAVs With Dynamic Obstacles](https://mlsanity.com/paper/2008.00792/) (2020)
- [Visual Navigation in Real-World Indoor Environments Using End-to-End Deep Reinforcement Learning](https://mlsanity.com/paper/2010.10903/) (2020)
- [Position-Agnostic Autonomous Navigation in Vineyards with Deep Reinforcement Learning](https://mlsanity.com/paper/2206.14155/) (2022)
- [Depth-CUPRL: Depth-Imaged Contrastive Unsupervised Prioritized Representations in Reinforcement Learning for Mapless Navigation of Unmanned Aerial Vehicles](https://mlsanity.com/paper/2206.15211/) (2022)
- [Deep Learning on Mobile Devices Through Neural Processing Units and Edge Computing](https://mlsanity.com/paper/2112.02439/) (2021)
- [OpenStreetMap-based Autonomous Navigation With LiDAR Naive-Valley-Path Obstacle Avoidance](https://mlsanity.com/paper/2108.09117/) (2021)
- [Vision-based Distributed Multi-UAV Collision Avoidance via Deep Reinforcement Learning for Navigation](https://mlsanity.com/paper/2203.02650/) (2022)

## Abstract (arXiv)

Fixed-wing Unmanned Aerial Vehicles (UAVs) are one of the most commonly used platforms for the burgeoning Low-altitude Economy (LAE) and Urban Air Mobility (UAM), due to their long endurance and high-speed capabilities. Classical obstacle avoidance systems, which rely on prior maps or sophisticated sensors, face limitations in unknown low-altitude environments and small UAV platforms. In response, this paper proposes a lightweight deep reinforcement learning (DRL) based UAV collision avoidance system that enables a fixed-wing UAV to avoid unknown obstacles at cruise speed over 30m/s, with only onboard visual sensors. The proposed system employs a single-frame image depth inference module with a streamlined network architecture to ensure real-time obstacle detection, optimized for edge computing devices. After that, a reinforcement learning controller with a novel reward function is designed to balance the target approach and flight trajectory smoothness, satisfying the specific dynamic constraints and stability requirements of a fixed-wing UAV platform. An adaptive entropy adjustment mechanism is introduced to mitigate the exploration-exploitation trade-off inherent in DRL, improving training convergence and obstacle avoidance success rates. Extensive software-in-the-loop and hardware-in-the-loop experiments demonstrate that the proposed framework outperforms other methods in obstacle avoidance efficiency and flight trajectory smoothness and confirm the feasibility of implementing the algorithm on edge devices. The source code is publicly available at urlhttps://github.com/ch9397/FixedWing-MonoPPO.