# Self-Supervised Path Planning in UAV-aided Wireless Networks based on Active Inference

arXiv: https://arxiv.org/abs/2403.13827 · HTML: https://mlsanity.com/paper/2403.13827/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in World models for navigation, one of 11 directions in World models.
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 6 Mar 2024.

## Placed in

- World models / [World models for navigation](https://mlsanity.com/wm/world-models-for-navigation/)

## Builds on (most cited first, 3 of 3)

- [UAV Communications for 5G and Beyond: Recent Advances and Future Trends](https://mlsanity.com/paper/1901.06637/) (2019)
- [Cellular-Enabled UAV Communication: A Connectivity-Constrained Trajectory Optimization Perspective](https://mlsanity.com/paper/1805.07182/) (2018)
- [Worst Case and Probabilistic Analysis of the 2-Opt Algorithm for the TSP](https://mlsanity.com/paper/2302.06889/) (2023)

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

- [Bayesian Active Inference for Intelligent UAV Anti-Jamming and Adaptive Trajectory Planning](https://mlsanity.com/paper/2512.05711/) (2025)

## Abstract (arXiv)

This paper presents a novel self-supervised path-planning method for UAV-aided networks. First, we employed an optimizer to solve training examples offline and then used the resulting solutions as demonstrations from which the UAV can learn the world model to understand the environment and implicitly discover the optimizer's policy. UAV equipped with the world model can make real-time autonomous decisions and engage in online planning using active inference. During planning, UAV can score different policies based on the expected surprise, allowing it to choose among alternative futures. Additionally, UAV can anticipate the outcomes of its actions using the world model and assess the expected surprise in a self-supervised manner. Our method enables quicker adaptation to new situations and better performance than traditional RL, leading to broader generalizability.