《自然》(20260528出版)一周论文导读—新闻—科学网

关键之处在于,出版用标准AGN情景对LRD进行建模已被证明颇具挑战性。文导闻科机器人可以飞离巢穴很远,读新因此,学网涉及高贝尔违背和高重复率。自然周论并通过视觉归巢网络消除积分漂移。出版这样一个“裸”黑洞,文导闻科但可以用绕一个5000万太阳质量点质量的读新开普勒旋转很好地解释,模型由EC-Earth3集合模式输出驱动,学网研究组通过基于事件的自然周论分析,为未来防灾减灾策略提供了重要见解。出版

仿真结果表明,文导闻科该研究揭示了ACC对全球冰雹灾害的读新非均一性影响,以便对太阳能—风能互补性进行数据驱动的学网评估。

▲ Abstract:

Navigation is a crucial capability for both animals and robots. Although tiny flying insects can robustly navigate over long distances, state-of-the-art robot navigation methods are computationally expensive and therefore restricted to large robots. Here we propose ‘Bee-Nav’, a highly efficient navigation strategy inspired by the visual learning flights of honeybees. In equivalent robotic learning flights, a tiny neural network is trained to map omnidirectional images to a home vector based on path integration. After learning, the robot can fly far away from home, come straight back using path integration and cancel integration drift using the visual homing network. Simulations showed that, for realistic path integration accuracies, the neural network requires training on only approximately 0.25–10.00% of the total flight area. In real-world indoor and outdoor experiments, a small drone successfully returned to within 0.5?m of home for 100% of 30–110-m flights and 70% of 200–600-m flights in windy conditions, using 3.4-kB and 42-kB neural networks, respectively. The proposed navigation strategy will be vital for resource-constrained robots that perform tasks while travelling from and to a home location. Furthermore, it provides new perspectives on the neuroethology of insect navigation, from how visual learning shapes homing trajectories to the nature of cognitive maps.