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Related Experiment Videos

RIS assisted energy aware multi agent adaptive SAC for UAV aided IoT network path planning and obstacle avoidance.

Md Najmul Mowla1, Davood Asadi2, Khaled M Rabie3

  • 1Department of Electrical and Electronics Engineering, Graduate School, Adana Alparslan Türkeş Science and Technology University, 1250, Adana, Turkey. najmulmowla01@gmail.com.

Scientific Reports
|July 13, 2026
PubMed
Summary

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This study introduces a Multi-Agent Soft Actor-Critic (MASAC) framework for Unmanned Aerial Vehicle (UAV) navigation. MASAC enhances energy efficiency and mission success in 6G Internet of Things (IoT) environments with Reconfigurable Intelligent Surfaces (RIS).

Area of Science:

  • Robotics and Autonomous Systems
  • Wireless Communications
  • Artificial Intelligence

Background:

  • Unmanned Aerial Vehicles (UAVs) are crucial for next-generation Internet of Things (IoT) infrastructures, enabling real-time data collection and agile operations.
  • Safe and energy-efficient multi-UAV navigation faces challenges from dynamic obstacles, limited energy, and 6G communication constraints.
  • Centralized coordination for multiple UAVs is often costly and complex in dynamic IoT environments.

Purpose of the Study:

  • To introduce a Multi-Agent Soft Actor-Critic (MASAC) framework for optimizing UAV path planning and energy-aware coordination.
  • To enhance navigation efficiency and mission success for UAVs operating within Reconfigurable Intelligent Surface (RIS) assisted 6G IoT environments.
  • To develop an energy-aware navigation strategy that leverages RIS for recharging and optimizes trajectories for connectivity.

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Main Methods:

  • Developed a Multi-Agent Soft Actor-Critic (MASAC) framework integrating entropy-regularized actor-critic learning.
  • Incorporated Reconfigurable Intelligent Surface (RIS) aware reward shaping for energy-aware navigation and recharging.
  • Utilized a lightweight convolutional policy network to encode spatial information for efficient policy learning.

Main Results:

  • Achieved a 100% mission success rate, a 33.3% relative improvement over baseline methods.
  • Increased Reconfigurable Intelligent Surface (RIS) recharge utilization by approximately 33%.
  • Enhanced 6G connectivity by 6% and cumulative reward by 23%, with low latency (38 ms per UAV).

Conclusions:

  • The MASAC framework provides a significant advancement in energy-efficient UAV navigation for RIS-assisted 6G IoT environments.
  • The study establishes a simulation benchmark for UAV navigation under realistic operational constraints.
  • MASAC demonstrates superior performance in mission success, energy utilization, connectivity, and reward compared to existing methods.