Robustness of the Infomax Network for View Based Navigation of Long Routes
Robustness of the Infomax Network for View Based Navigation of Long Routes
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用于基于视图的长路线导航的 Infomax 网络的鲁棒性
DOI：
10.1162/isal_a_00645
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Amin A
中科院分区：
文献类型：
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作者：
Amin A
昆虫启发的导航策略有可能在功率受限的情况下解锁机器人导航，因为它们可以在有限的计算资源下有效地发挥作用。其中一种策略，基于熟悉度的导航，已经成功地使用在在线机器人应用中使用Infomax学习规则训练的单层神经网络导航了高达60米的路线。在这里，我们挑战Infomax导航更长的路线，调查性能，视图大小，视图采集率和网络大小之间的关系。通过这样做，我们确定了Infomax有效运行的参数，并探索了它失败的配置文件。我们表明，有效的记忆熟悉的意见是可能的，比以前尝试的更长的路线，但这种长度减少输入视图尺寸。在选择一个理想的视图采集速率，我们还表明，这必须增加与路由长度一致的性能。在调查小型，低功耗机器人的适用性，我们证明，计算和内存的节省可以通过减少网络的大小与同等的性能。最后，我们调查了故障发生的配置文件，展示了增加的混乱发生在整个路线，因为它延伸的长度。这些发现被用于为昆虫导航理论提供信息，并改善长路线基于视图的导航的实际部署。
Insect inspired navigation strategies have the potential to unlock robotic navigation in power-constrained scenarios as they can function effectively with limited computational resources. One such strategy, familiarity-based navigation, has successfully navigated routes of up to 60m using a single layer neural network trained with an Infomax learning rule in online robotic applications. Here we challenge Infomax to navigate longer routes, investigating the relationship between performance, view size, view acquisition rate and network size. By doing so, we determine the parameters at which Infomax operates effectively and explore the profile with which it fails. We show that effective memorisation of familiar views is possible for longer routes than previously attempted, but that this length decreases for reduced input view dimensions. In the selection of an ideal view acquisition rate, we also show that this must be increased with route length for consistent performance. In investigating the applicability to small, lower-power robots, we demonstrate that computational and memory savings may be made with equivalent performance by reducing the network size. Finally, we investigate the profile with which failure occurs, demonstrating increased confusion occurring across the route as it extends in length. These findings are being used to inform theories of insect navigation and improve practical deployment of view based navigation for long routes.