Resource-constrained robots often suffer from energy inefficiencies, underutilized computational abilities due to inadequate task allocation, and a lack of robustness in dynamic environments, all of which strongly affect their performance. This paper introduces DREAM - Decentralized Reinforcement Learning for Exploration and Efficient Energy Management in Multi-Robot Systems, a comprehensive framework that optimizes the allocation of resources for efficient exploration. It advances beyond conventional heuristic-based task planning as observed conventionally. The framework incorporates Operational Range Estimation using Reinforcement Learning to perform exploration and obstacle avoidance in unfamiliar terrains. DREAM further introduces an Energy Consumption Model for goal allocation, thereby ensuring mission completion under constrained resources using a Graph Neural Network. This approach also ensures that the entire Multi-Robot System can survive for an extended period of time for further missions compared to the conventional approach of randomly allocating goals, which compromises one or more agents. Our approach adapts to prioritizing agents in real-time, showcasing remarkable resilience against dynamic environments. This robust solution was evaluated in various simulated environments, demonstrating adaptability and applicability across diverse scenarios. We observed a substantial improvement of about 25% over the baseline method, leading the way for future research in resource-constrained robotics.

翻译：资源受限的机器人常因能量效率低下、任务分配不足导致计算能力未充分利用，以及在动态环境中缺乏鲁棒性，这些问题严重影响了其性能。本文提出DREAM——一种面向多机器人系统探索与高效能量管理的去中心化强化学习框架，该综合框架通过优化资源配置以实现高效探索。其突破了传统基于启发式的任务规划方法。该框架引入基于强化学习的运行范围估计模块，可在陌生地形中执行探索与避障。DREAM进一步提出基于能量消耗模型的目标分配策略，借助图神经网络在资源约束下确保任务完成。相较于随机分配目标（此方法会导致一个或多个智能体受损）的传统方案，本方法还能确保整个多机器人系统具备更长的持续运行时间以执行后续任务。我们的方法能够实时调整智能体优先级，在动态环境中展现出卓越的韧性。该鲁棒解决方案在多种仿真环境中进行了评估，验证了其在不同场景下的适应性与实用性。相较于基准方法，我们观察到约25%的性能显著提升，这为资源受限机器人领域的未来研究指明了方向。