Geometry-based navigation in the dark: layout symmetry facilitates spatial learning in the house cricket, Acheta domesticus, in the absence of visual cues

Neural and sensory basis of homing behaviour in the invasive cane toad, Rhinella marina

The behavioural, sensory and neural bases of vertebrate navigation are primarily described in mammals and birds. While many studies have explored amphibian navigation, none have characterized brain activity associated with navigation in the wild. To address this knowledge gap, we conducted a study on navigation in the cane toad, Rhinella marina. First, we performed a translocation experiment to describe how invasive cane toads in Hawaii navigate home and observed homing following displacements of up to 1 km. Next, we tested the effect of olfactory and magnetosensory manipulations on homing, as these senses are most commonly associated with amphibian navigation. We found that neither ablation alone prevents homing, further supporting that toad navigation is multimodal. Finally, we tested the hypothesis that the medial pallium, the amphibian homologue to the hippocampus, is involved in homing. Our comparisons of neural activity revealed evidence supporting a conservation of neural structures associated with navigation across vertebrates consistent with neural models of amphibian spatial cognition from recent laboratory studies. Our work furthers our evolutionary understanding of spatial behaviour and cognition in vertebrates and lays a foundation for studying the behavioural, sensory and neural bases of navigation in an invasive amphibian.

Investigating boundary-geometry use by whip spiders (Phrynus marginemaculatus) during goal-directed navigation

Previous studies have shown that whip spiders (Amblypygi) can use a variety of cues to navigate to and recognize a home refuge. The current study aimed to determine whether whip spiders were capable of using the boundary geometry of an experimental space (geometric information) to guide goal-directed navigation and to investigate any preferential use of geometric or feature (visual) information. Animals were first trained to find a goal location situated in one corner of a rectangular arena (geometric information) fronting a dark-green-colored wall, which created a brightness contrast with the other three white walls (feature information). Various probe trials were then implemented to determine cue use. It was found that animals were capable of directing their choice behavior towards geometrically correct corners at a rate significantly higher than chance, even when the feature cue was removed. By contrast, choice behavior dropped to random chance when geometric information was removed (test in a square arena) and only feature information remained. Choice behavior was also reduced to chance when geometric and feature information were set in conflict (by moving the feature cue to one of the longer walls in the rectangular arena). The data thus suggest that whip spiders are capable of using geometric information to guide goal-directed navigation and that geometric information is preferred over feature guidance, although a feature cue may set the context for activating geometry-guided navigation. Experimental design limitations and future directions are discussed.

A Novel Obstacle Traversal Method for Multiple Robotic Fish Based on Cross-Modal Variational Autoencoders and Imitation Learning

Precision control of multiple robotic fish visual navigation in complex underwater environments has long been a challenging issue in the field of underwater robotics. To address this problem, this paper proposes a multi-robot fish obstacle traversal technique based on the combination of cross-modal variational autoencoder (CM-VAE) and imitation learning. Firstly, the overall framework of the robotic fish control system is introduced, where the first-person view of the robotic fish is encoded into a low-dimensional latent space using CM-VAE, and then different latent features in the space are mapped to the velocity commands of the robotic fish through imitation learning. Finally, to validate the effectiveness of the proposed method, experiments are conducted on linear, S-shaped, and circular gate frame trajectories with both single and multiple robotic fish. Analysis reveals that the visual navigation method proposed in this paper can stably traverse various types of gate frame trajectories. Compared to end-to-end learning and purely unsupervised image reconstruction, the proposed control strategy demonstrates superior performance, offering a new solution for the intelligent navigation of robotic fish in complex environments.