Reliably navigating to a distant goal remains a major challenge in robotics. In contrast, animals such as rats and pigeons can perform goal-directed navigation with great reliability. Evidence from neural science and ethology suggests that various species represent the spatial space as a topological template, with which they can actively evaluate future navigation uncertainty and plan reliable/safe paths to distant goals. While topological navigation models have been deployed in mobile robots, relatively little inspiration has drawn upon biology in terms of topological mapping and active path planning. In this paper, we propose a novel bio-inspired topological navigation model, which consists of topological map construction, active path planning and path execution, for aerial mobile robots with visual landmark recognition and compass orientation capability. To mimic the topological spatial representation, the model firstly builds the topological nodes based on the reliability of visual landmarks, and constructs the edges based on the compass accuracy. Then a reward diffusion algorithm akin to animals’ path evaluation process is developed. The diffusion process takes the topological structure and landmark reliability into consideration, which helps the agent to construct the path with visually reliable nodes. In the path execution process, the agent combines orientation guidance and landmark recognition to estimate its position. To evaluate the performance of the proposed navigation model, a systematic series of experiments were conducted in a range of challenging and varied real-world visual environments. The results show that the proposed model generates animal-like navigation behaviours, which avoids travelling across large visually aliased areas, such as forest and water regions, and achieves higher localization accuracy than navigating on the shortest paths.

可靠地导航到遥远的目标仍然是机器人技术的主要挑战。相反，诸如大鼠和鸽子之类的动物可以高度可靠地执行目标定向导航。来自神经科学和伦理学的证据表明，各种物种将空间空间表示为拓扑模板，利用它们可以主动评估未来的导航不确定性并计划通往遥远目标的可靠/安全路径。尽管拓扑导航模型已部署在移动机器人中，但在拓扑映射和主动路径规划方面，生物学方面的灵感相对较少。在本文中，我们提出了一种新颖的，受生物启发的拓扑导航模型，该模型由拓扑图构建，主动路径规划和路径执行，适用于具有视觉地标识别和指南针定向功能的空中移动机器人。为了模拟拓扑空间表示，该模型首先基于视觉界标的可靠性构建拓扑节点，然后根据罗盘精度构建边缘。然后，开发了一种类似于动物路径评估过程的奖励扩散算法。扩散过程考虑了拓扑结构和地标可靠性，这有助于代理使用视觉上可靠的节点构造路径。在路径执行过程中，代理将方向指导和地标识别相结合以估计其位置。为了评估所提出的导航模型的性能，在一系列充满挑战的现实世界视觉环境中进行了一系列系统的实验。