mlsanityData to 14 Oct 2026

Optimizing Search and Rescue UAV Connectivity in Challenging Terrain through Multi Q-Learning

arXiv 2405.10042 ↗First posted 16 May 2024cs.ROcited by 2

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

Using Unmanned Aerial Vehicles (UAVs) in Search and rescue operations (SAR) to navigate challenging terrain while maintaining reliable communication with the cellular network is a promising approach. This paper suggests a novel technique employing a reinforcement learning multi Q-learning algorithm to optimize UAV connectivity in such scenarios. We introduce a Strategic Planning Agent for efficient path planning and collision awareness and a Real-time Adaptive Agent to maintain optimal connection with the cellular base station. The agents trained in a simulated environment using multi Q-learning, encouraging them to learn from experience and adjust their decision-making to diverse terrain complexities and communication scenarios. Evaluation results reveal the significance of the approach, highlighting successful navigation in environments with varying obstacle densities and the ability to perform optimal connectivity using different frequency bands. This work paves the way for enhanced UAV autonomy and enhanced communication reliability in search and rescue operations.

Key facts. Cited by 2 papers in the map (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 3 of its 19 references that are in the map. First posted on arXiv 16 May 2024.

Builds on

3 of 19 references.

References · 3

  1. 2019Obstacle-aware Adaptive Informative Path Planning for UAV-based Target Searchcited by 18
  2. 2024Machine Learning-based xApp for Dynamic Resource Allocation in O-RAN Networkscited by 5
  3. 2023FLCC: Efficient Distributed Federated Learning on IoMT over CSMA/CAcited by 2

Built on by

Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025A Comprehensive Survey on Multi-Agent Cooperative Decision-Making: Scenarios, Approaches, Challenges and Perspectivescited by 27
  2. 2025Semantic-Aware Edge Intelligence for UAV Handover in 6G Networkscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Towards Robust Multi-UAV Collaboration: MARL with Noise-Resilient Communication and Attention Mechanisms
  2. 2025Maximizing UAV Cellular Connectivity with Reinforcement Learning for BVLoS Path Planning
  3. 2024Dynamic Q-planning for Online UAV Path Planning in Unknown and Complex Environments
  4. 2025Scalable UAV Multi-Hop Networking via Multi-Agent Reinforcement Learning with Large Language Models
  5. 2025Integrated Communication and Control for Energy-Efficient UAV Swarms: A Multi-Agent Reinforcement Learning Approach
  6. 2025Green Emergency Communications in RIS- and MA-Assisted Multi-UAV SAGINs: A Partially Observable Reinforcement Learning Approach
  7. 2025Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning
  8. 2025Multi-Agent Deep Reinforcement Learning for Collaborative UAV Relay Networks under Jamming Atatcks