# Autonomous Drone Swarm Navigation and Multi-target Tracking in 3D Environments with Dynamic Obstacles

arXiv: https://arxiv.org/abs/2202.06253 · HTML: https://mlsanity.com/paper/2202.06253/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 64 references that are in the map.
- First posted on arXiv 13 Feb 2022.

## Builds on (most cited first, 18 of 18)

- [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)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Deep Reinforcement Learning that Matters](https://mlsanity.com/paper/1709.06560/) (2017)
- [Teacher-Student Curriculum Learning](https://mlsanity.com/paper/1707.00183/) (2017)
- [An Introduction to Deep Reinforcement Learning](https://mlsanity.com/paper/1811.12560/) (2018)
- [Sample Efficient Actor-Critic with Experience Replay](https://mlsanity.com/paper/1611.01224/) (2016)
- [Truly Proximal Policy Optimization](https://mlsanity.com/paper/1903.07940/) (2019)
- [Deep Reinforcement Learning for Swarm Systems](https://mlsanity.com/paper/1807.06613/) (2018)
- [Guided Deep Reinforcement Learning for Swarm Systems](https://mlsanity.com/paper/1709.06011/) (2017)
- [An Adaptive Clipping Approach for Proximal Policy Optimization](https://mlsanity.com/paper/1804.06461/) (2018)
- [PPO-CMA: Proximal Policy Optimization with Covariance Matrix Adaptation](https://mlsanity.com/paper/1810.02541/) (2018)
- [Proximal Policy Optimization via Enhanced Exploration Efficiency](https://mlsanity.com/paper/2011.05525/) (2020)
- [Swarm coordination of mini-UAVs for target search using imperfect sensors](https://mlsanity.com/paper/1901.02885/) (2019)
- [Sum-Rate Maximization for UAV-assisted Visible Light Communications using NOMA: Swarm Intelligence meets Machine Learning](https://mlsanity.com/paper/2101.03498/) (2021)

## Built on by (most cited first, 4 of 4)

- [A New Deep Boosted CNN and Ensemble Learning based IoT Malware Detection](https://mlsanity.com/paper/2212.08008/) (2022)
- [Advanced Hybrid Transformer LSTM Technique with Attention and TS Mixer for Drilling Rate of Penetration Prediction](https://mlsanity.com/paper/2508.05210/) (2025)
- [A Novel Decision Ensemble Framework: Customized Attention-BiLSTM and XGBoost for Speculative Stock Price Forecasting](https://mlsanity.com/paper/2401.11621/) (2024)
- [Neural Network Verification for Gliding Drone Control: A Case Study](https://mlsanity.com/paper/2505.00622/) (2025)

## Abstract (arXiv)

Autonomous modeling of artificial swarms is necessary because manual creation is a time intensive and complicated procedure which makes it impractical. An autonomous approach employing deep reinforcement learning is presented in this study for swarm navigation. In this approach, complex 3D environments with static and dynamic obstacles and resistive forces (like linear drag, angular drag, and gravity) are modeled to track multiple dynamic targets. Moreover, reward functions for robust swarm formation and target tracking are devised for learning complex swarm behaviors. Since the number of agents is not fixed and has only the partial observance of the environment, swarm formation and navigation become challenging. In this regard, the proposed strategy consists of three main phases to tackle the aforementioned challenges: 1) A methodology for dynamic swarm management, 2) Avoiding obstacles, Finding the shortest path towards the targets, 3) Tracking the targets and Island modeling. The dynamic swarm management phase translates basic sensory input to high level commands to enhance swarm navigation and decentralized setup while maintaining the swarms size fluctuations. While, in the island modeling, the swarm can split into individual subswarms according to the number of targets, conversely, these subswarms may join to form a single huge swarm, giving the swarm ability to track multiple targets. Customized state of the art policy based deep reinforcement learning algorithms are employed to achieve significant results. The promising results show that our proposed strategy enhances swarm navigation and can track multiple static and dynamic targets in complex dynamic environments.