Trajectory Planner CDT-RRT* for Car-Like Mobile Robots toward Narrow and Cluttered Environments

In order to achieve the safe and efficient navigation of mobile robots, it is essential to consider both the environmental geometry and kinodynamic constraints of robots. We propose a trajectory planner for car-like robots on the basis of the Dual-Tree RRT (DT-RRT). DT-RRT utilizes two tree structures in order to generate fast-growing trajectories under the kinodynamic constraints of robots. A local trajectory generator has been newly designed for car-like robots. The proposed scheme of searching a parent node enables the efficient generation of safe trajectories in cluttered environments. The presented simulation results clearly show the usefulness and the advantage of the proposed trajectory planner in various environments.

Space syntax visibility graph analysis is not robust to changes in spatial and temporal resolution

Space syntax is an influential framework for quantifying the relationship between environmental geometry and human behavior. Although many studies report high syntactic–behavioral correlations, previous pedestrian data were collected at low spatiotemporal resolutions, and data transformations and sampling strategies vary widely; here, we systematically test the robustness of space syntax’s predictive strength by examining how these factors impact correlations. We used virtual reality and motion tracking to correlate 30 syntactic measures with high resolution walking trajectories downsampled at 10 grid resolutions and subjected to various log transformations. Overall, correlations declined with increasing grid resolution and were sensitive to data transformations. Moreover, simulations revealed spuriously high correlations (e.g. R2 = 1) with sparsely sampled data (<23 locations). These results strongly suggest that syntactic–behavioral correlations are not robust to changes in spatiotemporal resolution, and that high correlations obtained in previous studies could be inflated due to transformations, data resolution, or sampling strategies.

Spontaneous object-location memory based on environmental geometry is impaired by both hippocampal and dorsolateral striatal lesions

We examined the role of the hippocampus and the dorsolateral striatum in the representation of environmental geometry using a spontaneous object recognition procedure. Rats were placed in a kite-shaped arena and allowed to explore two distinctive objects in each of the right-angled corners. In a different room, rats were then placed into a rectangular arena with two identical copies of one of the two objects from the exploration phase, one in each of the two adjacent right-angled corners that were separated by a long wall. Time spent exploring these two objects was recorded as a measure of recognition memory. Since both objects were in different locations with respect to the room (different between exploration and test phases) and the global geometry (also different between exploration and test phases), differential exploration of the objects must be a result of initial habituation to the object relative to its local geometric context. The results indicated an impairment in processing the local geometric features of the environment for both hippocampus and dorsolateral striatum lesioned rats compared with sham-operated controls, though a control experiment showed these rats were unimpaired in a standard object recognition task. The dorsolateral striatum has previously been implicated in egocentric route-learning, but the results indicate an unexpected role for the dorsolateral striatum in processing the spatial layout of the environment. The results provide the first evidence that lesions to the hippocampus and dorsolateral striatum impair spontaneous encoding of local environmental geometric features.

Neurobiological successor features for spatial navigation

The hippocampus has long been observed to encode a representation of an animal’s position in space. Recent evidence suggests that the nature of this representation is somewhat predictive and can be modelled by learning a successor representation (SR) between distinct positions in an environment. However, this discretisation of space is subjective making it difficult to formulate predictions about how some environmental manipulations should impact the hippocampal representation. Here we present a model of place and grid cell firing as a consequence of learning a SR from a basis set of known neurobiological features – boundary vector cells (BVCs). The model describes place cell firing as the successor features of the SR, with grid cells forming a low-dimensional representation of these successor features. We show that the place and grid cells generated using the BVC-SR model provide a good account of biological data for a variety of environmental manipulations, including dimensional stretches, barrier insertions, and the influence of environmental geometry on the hippocampal representation of space.

Entorhinal velocity signals reflect environmental geometry

SummaryThe entorhinal cortex contains neural signals for representing self-location, including grid cells that fire in periodic locations and velocity signals that encode an animal’s speed and head direction. Recent work revealed that the size and shape of the environment influences grid patterns. Whether entorhinal velocity signals are equally influenced or provide a universal metric for self-motion across environments remains unknown. Here, we report that changes to the size and shape of the environment result in re-scaling in entorhinal speed codes. Moreover, head direction cells re-organize in an experience-dependent manner to align with the axis of environmental change. A knockout mouse model allows a dissociation of the coordination between cell types, with grid and speed, but not head direction, cells responding in concert to environmental change. These results align with predictions of grid cell attractor models and point to inherent flexibility in the coding features of multiple functionally-defined entorhinal cell types.

Rapid invariant encoding of scene layout in human OPA

SUMMARYSuccessful visual navigation requires a sense of the geometry of the local environment. How do our brains extract this information from retinal images? Here we visually presented scenes with all possible combinations of five scene-bounding elements (left, right and back wall, ceiling, floor) to human subjects during functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG). The fMRI response patterns in the scene-responsive occipital place area (OPA) reflected scene layout with invariance to changes in surface texture. This result contrasted sharply with the primary visual cortex (V1), which reflected low-level image features of the stimuli, and parahippocampal place area (PPA), which showed better texture than layout decoding. MEG indicated that the texture-invariant scene-layout representation is computed from visual input within ~100 ms, suggesting a rapid computational mechanism. Taken together, these results suggest that the cortical representation underlying our instant sense of the environmental geometry is located in OPA.