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.

翻译：机器人技术有望支持环境监测与自然灾害管理，在此类场景中，决策必须在不确定性、资源限制及严格操作约束下做出。在野火等关键任务中，机器人不仅需要以足够置信度识别危险事件，还需管理能量消耗及检测时间。本文提出ED3R——一种面向不确定性环境下野火检测的能量感知分布式框架。ED3R实现了机器人与远程控制器之间的层级协同决策：远程控制器决定机器人的运动，而机器人感知环境并自主决策在何处（机载或远程）及如何执行野火检测。共同目标是在满足所需检测置信度的同时，最小化机器人任何操作消耗的能量。ED3R进一步集成了避障机制、冗余探索抑制、自适应任务提前完成功能，并通过自定义惩罚函数确保可行性。此外，ED3R通过分布式神经回归模型引入前瞻能力，使智能体能够在执行前评估候选策略以预测未来发展。该框架通过逼真的机器人仿真、消融研究及基线对比进行评估。总体而言，ED3R的任务成功率达97.18%；在最严苛的任务中，相比基线方法，其能量消耗降低最高达36.4%，野火检测速度提升最高达41%。