Traditional path planning methods, such as A* and probabilistic roadmap, are seriously limited by the resolution and the number of samples that cannot meet the needs of complex indoor environments. Therefore, artificial neural network-based methods have become the mainstream due to the strong learning ability and robustness. In this paper, the neural network is used to control the agent according to the concentration and distance information, and a novel approach called adaptive neural evolution of augmenting topologies is proposed to optimize the neural network to improve its performance. Besides, the objective function is constructed by combining the pollution concentration and the residual energy of the agent to save energy and search time. Experiments show that our approach successfully finds an optimal path to pollution sources in different indoor environments while achieving energy saving and obstacle avoidance.

传统的路径规划方法（例如A *和概率路线图）受到分辨率和无法满足复杂室内环境需求的样本数量的严重限制。因此，由于强大的学习能力和鲁棒性，基于人工神经网络的方法已成为主流。本文利用神经网络根据集中度和距离信息来控制代理，并提出了一种新的方法，称为增强拓扑的自适应神经进化，以优化神经网络以提高其性能。此外，目标函数是通过将污染物的浓度和药剂的剩余能量相结合来构造的，从而节省了能量并缩短了搜索时间。