HMA-SAR: Multi-Agent Search and Rescue for Unknown Located Dynamic Targets in Completely Unknown Environments

Multi-Agent Search and Rescue (MASAR) tasks, challenged by unknown environments and the unpredictable move- mentsofunknowndynamictargets,sufferfrominefficienciesintra- ditional map coverage techniques which require repeated sweeps. Addressing this, our study introduces a novel MASAR framework based on Multi-Agent Reinforcement Learning (MARL), featur- ing innovative elements like state, reward, and network structure design, alongside a Heterogeneous Curriculum Training algorithm and a hybrid decision mechanism. These components collectively enhance performance in dynamic environments, improve model generalization, and mitigate issues like sparse rewards and policy bias.Ingridmapsimulations,ourapproach,HMA-SAR(Heteroge- neous Multi-Agent Search and Rescue Framework), demonstrated consistent superiority over the traditional frontier-based method and other MARL algorithms, in metrics such as success rate, steps count, and the number of targets fetched. The practical applicabil- ity of our approach was further validated through experiments in Gazebo and real-world scenarios. Additionally, scalability tests in grid maps revealed substantial improvements in success rates and task completion times with increased agent deployment.