# Learning-Based UAV Trajectory Optimization with Collision Avoidance and Connectivity Constraints

arXiv: https://arxiv.org/abs/2104.06256 · HTML: https://mlsanity.com/paper/2104.06256/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 36 references that are in the map.
- First posted on arXiv 3 Apr 2021.

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

- [Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning](https://mlsanity.com/paper/1809.08835/) (2018)
- [Socially Aware Motion Planning with Deep Reinforcement Learning](https://mlsanity.com/paper/1703.08862/) (2017)
- [Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning](https://mlsanity.com/paper/1805.01956/) (2018)
- [Collision Avoidance in Pedestrian-Rich Environments with Deep Reinforcement Learning](https://mlsanity.com/paper/1910.11689/) (2019)
- [Accessing From The Sky: A Tutorial on UAV Communications for 5G and Beyond](https://mlsanity.com/paper/1903.05289/) (2019)
- [Simultaneous Navigation and Radio Mapping for Cellular-Connected UAV with Deep Reinforcement Learning](https://mlsanity.com/paper/2003.07574/) (2020)
- [Radio Map Based 3D Path Planning for Cellular-Connected UAV](https://mlsanity.com/paper/1912.00021/) (2019)
- [Cellular-Connected UAVs over 5G: Deep Reinforcement Learning for Interference Management](https://mlsanity.com/paper/1801.05500/) (2018)
- [Cellular-Enabled UAV Communication: A Connectivity-Constrained Trajectory Optimization Perspective](https://mlsanity.com/paper/1805.07182/) (2018)
- [Cellular Connectivity for UAVs: Network Modeling, Performance Analysis and Design Guidelines](https://mlsanity.com/paper/1804.08121/) (2018)
- [Impact of 3D UWB Antenna Radiation Pattern on Air-to-Ground Drone Connectivity](https://mlsanity.com/paper/1810.01442/) (2018)
- [Connectivity-Aware UAV Path Planning with Aerial Coverage Maps](https://mlsanity.com/paper/1905.05926/) (2019)
- [Performance Analysis for Practical Unmanned Aerial Vehicle Networks with LoS/NLoS Transmissions](https://mlsanity.com/paper/1804.00811/) (2018)
- [Cellular Coverage-Aware Path Planning for UAVs](https://mlsanity.com/paper/1905.03064/) (2019)
- [Power Efficient Trajectory Optimization for the Cellular-Connected Aerial Vehicles](https://mlsanity.com/paper/1906.09520/) (2019)
- [A Double Q-Learning Approach for Navigation of Aerial Vehicles with Connectivity Constraint](https://mlsanity.com/paper/2002.10563/) (2020)

## Built on by (most cited first, 3 of 3)

- [Jamming-Resilient Path Planning for Multiple UAVs via Deep Reinforcement Learning](https://mlsanity.com/paper/2104.04477/) (2021)
- [MADRL-based UAVs Trajectory Design with Anti-Collision Mechanism in Vehicular Networks](https://mlsanity.com/paper/2402.03342/) (2024)
- [Navigation in a Three-Dimensional Urban Flow using Deep Reinforcement Learning](https://mlsanity.com/paper/2510.25679/) (2025)

## Abstract (arXiv)

Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks, and determining collision-free trajectories for multiple UAVs while satisfying requirements of connectivity with ground base stations (GBSs) is a challenging task. In this paper, we first reformulate the multi-UAV trajectory optimization problem with collision avoidance and wireless connectivity constraints as a sequential decision making problem in the discrete time domain. We, then, propose a decentralized deep reinforcement learning approach to solve the problem. More specifically, a value network is developed to encode the expected time to destination given the agent's joint state (including the agent's information, the nearby agents' observable information, and the locations of the nearby GBSs). A signal-to-interference-plus-noise ratio (SINR)-prediction neural network is also designed, using accumulated SINR measurements obtained when interacting with the cellular network, to map the GBSs' locations into the SINR levels in order to predict the UAV's SINR. Numerical results show that with the value network and SINR-prediction network, real-time navigation for multi-UAVs can be efficiently performed in various environments with high success rate.