Towards an Autonomous Walking Robot for Planetary Surfaces

In this paper, recent progress in the development of the DLR Crawler - a six-legged, actively compliant walking robot prototype - is presented. The robot implements a walking layer with a simple tripod and a more complex biologically inspired gait. Using a variety of proprioceptive sensors, different reflexes for reactively crossing obstacles within the walking height are realised. On top of the walking layer, a navigation layer provides the ability to autonomously navigate to a predefined goal point in unknown rough terrain using a stereo camera. A model of the environment is created, the terrain traversability is estimated and an optimal path is planned. The difficulty of the path can be influenced by behavioral parameters. Motion commands are sent to the walking layer and the gait pattern is switched according to the estimated terrain difficulty. The interaction between walking layer and navigation layer was tested in different experimental setups

Maps and memories of space in the human brain

Mammalian navigation is mostly studied in rodents and humans. Due to ethical and methodological constraints, rodent research so far primarily targeted the neurophysiological mechanisms of navigation, while navigation studies in humans predominantly focused on navigational behavior and the cognitive processes involved in it. Although basic mechanisms of navigation seem well preserved across rodents and humans in general, human and rodent navigation also differ substantially in several aspects and it is not obvious how particular findings translate across both species. As a consequence, for many aspects of navigation, we do not know how processes on the cognitive level can be attributed to those on the cellular level, and, eventually, how particular navigation behavior can be causally related to neural activity. This knowledge gap is addressed in this thesis with two studies that extend our understanding of how findings from rodents and humans translate across both species. To this end, a framework was developed that combines human navigation in landmark-sparse virtual environments that resemble the open-field setups typically used to study spatially tuned neurons in rodents. Applying this framework, the first study presented in this thesis separates passive and active components during navigation, and investigates how varying navigational and spatial memory demands impact participants' brain activity. The results suggest that, first, certain brain regions primarily known for perception of static scenes are recruited during passive navigation, and also contribute information processing specifically relevant for active navigation; and that, second, the anterior medial hippocampus provides a coherent spatial representation of the current environment that is dependent on spatial memory. Using a similar setup, the second study investigates participants' spatial representation in more detail. The results show that, first, a model inspired by electrophysiological findings in rodents that explains location memory as a function of proximity to the environment's boundaries generally matches participants' behavior in a similar open-field environment; that, second, the model's explanatory power may be further improved when, in addition to the precision, also the accuracy of participants' location memory is considered; and that, finally, in a quadratic open-field environment, the diagonals also impact participant's spatial orientation and location memory. The findings reported in this thesis demonstrate that the framework applied in both studies allows for a detailed investigation of human navigation behavior, and the cognitive processes associated with it. It furthermore increases comparability of findings between human and rodent navigation, and may eventually help to better understand how neurophysiological processes are transformed into navigation behavior

Is there a pilot in the brain? Contribution of the self-positioning system to spatial navigation

International audienceSince the discovery of place cells, the hippocampus is thought to be the neural substrate of a cognitive map. The later discovery of head direction cells, grid cells and border cells, as well as of cells with more complex spatial signals, has led to the idea that there is a brain system devoted to providing the animal with the information required to achieve efficient navigation. Current questioning is focused on how these signals are integrated in the brain. In this review, we focus on the issue of how self-localization is performed in the hippocampal place cell map. To do so, we first shortly review the sensory information used by place cells and then explain how this sensory information can lead to two coding modes, respectively based on external landmarks (allothetic information) and self-motion cues (idiothetic information). We hypothesize that these two modes can be used concomitantly with the rat shifting from one mode to the other during its spatial displacements. We then speculate that sequential reactivation of place cells could participate in the resetting of self-localization under specific circumstances and in learning a new environment. Finally, we provide some predictions aimed at testing specific aspects of the proposed ideas

Sea ice-atmosphere interaction. Application of multispectral satellite data in polar surface energy flux estimates

Satellite data for the estimation of radiative and turbulent heat fluxes is becoming an increasingly important tool in large-scale studies of climate. One parameter needed in the estimation of these fluxes is surface temperature. To our knowledge, little effort has been directed to the retrieval of the sea ice surface temperature (IST) in the Arctic, an area where the first effects of a changing climate are expected to be seen. The reason is not one of methodology, but rather our limited knowledge of atmospheric temperature, humidity, and aerosol profiles, the microphysical properties of polar clouds, and the spectral characteristics of the wide variety of surface types found there. We have developed a means to correct for the atmospheric attenuation of satellite-measured clear sky brightness temperatures used in the retrieval of ice surface temperature from the split-window thermal channels of the advanced very high resolution radiometer (AVHRR) sensors on-board three of the NOAA series satellites. These corrections are specified for three different 'seasons' and as a function of satellite viewing angle, and are expected to be applicable to the perennial ice pack in the central Arctic Basin

Spatial Learning and Localization in Animals: A Computational Model and Its Implications for Mobile Robots

The ability to acquire a representation of spatial environment and the ability to localize within it are essential for successful navigation in a-priori unknown environments. The hippocampal formation is believed to play a key role in spatial learning and navigation in animals. This paper briefly reviews the relevant neurobiological and cognitive data and their relation to computational models of spatial learning and localization used in mobile robots. It also describes a hippocampal model of spatial learning and navigation and analyzes it using Kalman filter based tools for information fusion from multiple uncertain sources. The resulting model allows a robot to learn a place-based, metric representation of space in a-priori unknown environments and to localize itself in a stochastically optimal manner. The paper also describes an algorithmic implementation of the model and results of several experiments that demonstrate its capabilities

Neural and behavioral correlates of flexible navigation in complex space

This thesis focused on a primary aim: investigate behavioral and neural correlates of flexible spatial navigation using a variety of methods. To do so, we combined immersive virtual reality, real-world navigation, and neuroimaging to better understand the nuances in flexible behavior. Across all five studies discussed in this thesis, we utilized Sea Hero Quest as a baseline measure of spatial ability and prospective predictor for both behavioral and neural measures. Importantly, the work presented in this thesis aimed for novelty in methods. First, this thesis presents the first fMRI results from a dynamic navigation task with a continuously moving goal position – to our knowledge. Second, we found no evidence for a relationship between performance in Sea Hero Quest and either real-world wayfinding / spatial memory measures or related neural measures (hippocampal volume ratio) – in contrast to some recent findings. Last, a new task design looking at spatial performance in an urban version of Sea Hero Quest highlighted the importance of realism in task design. Overall, the work presented in this thesis adds to an understanding of flexible navigation and, importantly, highlights areas in which the field might advance