E2AR: An Energy-Efficient Augmented Reality Framework for Collaborative Multi-Drone Systems

Abstract

E2AR is an energy-efficient Augmented Reality framework specifically designed to facilitate collaborative tasks within multi-drone systems operating at the edge. It tackles the significant computational and communication overhead associated with real-time AR processing in highly resource-constrained environments. By optimizing data synchronization and computational offloading strategies, E2AR ensures seamless, low-latency augmented visualizations essential for complex cooperative drone operations while maximizing energy savings.

Report

E2AR: An Energy-Efficient Augmented Reality Framework for Collaborative Multi-Drone Systems

Key Highlights

• Focus Area: Development of an energy-efficient framework (E2AR) supporting Augmented Reality visualizations for human operators managing collaborative, multi-drone systems.
• Target Environment: Resource-constrained edge computing environments, where computational load, communication bandwidth, and power consumption are critical limiting factors.
• Core Challenge Addressed: Mitigating the high energy demands and latency inherent in processing complex AR data (e.g., visual overlays, sensor fusion) synchronized across multiple mobile platforms (drones).
• Goal: To enable dependable, real-time situational awareness and command capabilities via AR interfaces without rapidly depleting drone or edge server battery life.

Technical Details

• Framework Architecture: E2AR employs specific design decisions—likely involving selective computation offloading and data prioritization—to minimize the energy footprint of AR rendering and data exchange.
• Efficiency Mechanism: The 'Energy-Efficient' aspect implies leveraging techniques such as intelligent task scheduling, dynamic resource allocation between drones and edge servers, and optimized communication protocols tailored for AR synchronization.
• AR Workload Management: The framework must handle distributed workloads common in multi-UAV missions, including sensor data fusion, Simultaneous Localization and Mapping (SLAM) processing, and object tracking, ensuring that AR overlays are accurate and timely.
• Platform Context: Published in proceedings focusing on Edge Computing (SEC '25), indicating that the solution is designed for highly decentralized, low-power network deployments.

Implications

• Validation of Edge-Native Solutions: E2AR confirms the critical need for application-specific frameworks that treat energy efficiency as a primary design constraint for complex edge workloads like AR and video analytics.
• Relevance to RISC-V Ecosystem: Collaborative multi-drone systems are ideal deployment environments for specialized RISC-V based edge accelerators and System-on-Chips (SoCs). The high power efficiency and customization afforded by RISC-V are essential for integrating complex AR processing (e.g., vision processing, neural network inference) onto battery-powered UAV platforms or highly dense edge gateways. E2AR defines the performance envelope that such RISC-V hardware must meet.
• Future of Robotics and Edge AI: The successful implementation of E2AR accelerates the adoption of collaborative autonomous systems by providing a usable, power-optimized interface. This drives demand for modular, high-performance, low-power processing cores, aligning perfectly with the customizable and extensible nature of the RISC-V ISA for domain-specific acceleration.