用进化算法让机器人在复杂环境自主导航，成功率超80%。
Near-Driven Autonomous Rover Navigation in Complex Environments: Extensions to Urban Search-and-Rescue and Industrial Inspection
- 基于NEAT的神经进化方法，结合强化学习优化控制策略。
- 户外测试成功率达约80%，媲美顶尖深度强化学习模型。
- 适合救援、巡检等高风险场景，可作为深度学习补充方案。
本文研究基于神经进化增益拓扑（NEAT）的扩展神经进化方法在动态危险环境中的应用，适用于消防、城市搜救（USAR）及工业巡检等任务。通过拓展仿真环境至更大更复杂的场景，验证了NEAT在多种应用中的适应性。结合近期NEAT与强化学习进展，采用现代仿真框架提升真实性，并使用混合算法优化性能。实验表明，经NEAT演化出的控制器在户外测试中成功率约为80%，与当前最优深度强化学习方法相当，且具备更优的结构自适应能力。论文还探讨了跨任务迁移学习的优势，评估了NEAT在复杂三维导航中的有效性。贡献包括对NEAT在多样化自主任务中的评估，以及实际部署的考量，强调其作为深度强化学习替代或补充方案的潜力。
原文摘要 · Abstract (English)
This paper explores the use of an extended neuroevolutionary approach, based on NeuroEvolution of Augmenting Topologies (NEAT), for autonomous robots in dynamic environments associated with hazardous tasks like firefighting, urban search-and-rescue (USAR), and industrial inspections. Building on previous research, it expands the simulation environment to larger and more complex settings, demonstrating NEAT's adaptability across different applications. By integrating recent advancements in NEAT and reinforcement learning, the study uses modern simulation frameworks for realism and hybrid algorithms for optimization. Experimental results show that NEAT-evolved controllers achieve success rates comparable to state-of-the-art deep reinforcement learning methods, with superior structural adaptability. The agents reached ~80% success in outdoor tests, surpassing baseline models. The paper also highlights the benefits of transfer learning among tasks and evaluates the effectiveness of NEAT in complex 3D navigation. Contributions include evaluating NEAT for diverse autonomous applications and discussing real-world deployment considerations, emphasizing the approach's potential as an alternative or complement to deep reinforcement learning in autonomous navigation tasks.
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