ED3R: Energy-Aware Distributed Disaster Detection Enabled by Cooperative Robotic Agents

2026-06-16

AI Summary

This work addresses the challenge of efficient, low-energy wildfire detection in resource-constrained and uncertain environments by proposing the ED3R framework, which integrates hierarchical collaborative decision-making with forward-looking strategy evaluation. ED3R employs a distributed neural regression model to dynamically predict sensing utility, coupled with energy-aware path planning, an adaptive task termination mechanism, and obstacle avoidance algorithms to maintain high detection confidence while minimizing redundant exploration. Experimental results demonstrate that, compared to baseline approaches, ED3R reduces energy consumption by up to 36.4% and accelerates detection by as much as 41% in complex scenarios, achieving a task success rate of 97.18%.

Abstract

Robotics are expected to support environmental monitoring and natural disaster management, where decisions must be made under uncertainty, resource limitations, and strict operational constraints. In critical missions, such as wildfires, robotic agents must not only identify hazardous events with sufficient confidence, but also manage the energy cost and time until detection. This paper introduces ED3R, an energy-aware distributed framework for wildfire detection under uncertainty. ED3R enables hierarchical cooperative decision-making between a robot and a remote controller. The remote controller decides upon the robot's motion, while the robot senses the environment and decides where to execute the wildfire detection (onboard or remotely) and how. The common goal is to detect wildfires with a required confidence while minimizing the energy consumed by any robot operation. ED3R further integrates mechanisms to avoid nearby obstacles, prevent redundant exploration, enable adaptive early mission completion, and ensure feasibility through a custom penalty function. ED3R also introduces a forward-looking capability, enabled through distributed neural regression models that allow the agents to anticipate the future by evaluating candidate strategies before execution. The framework is evaluated through realistic robotics simulations, ablation studies, and baseline comparisons. Overall, ED3R achieves a mission success rate of up to 97.18%. Especially in the most demanding missions, it reduces energy consumption by up to 36.4% and detects wildfires up to 41% faster than baselines.

Problem

• wildfire detection
• energy-aware
• robotic agents
• uncertainty
• distributed decision-making

Innovation

• energy-aware
• distributed decision-making
• cooperative robotics
• neural regression
• wildfire detection