Social hymenopterans such as bees and ants are centralplace foragers; they regularly depart from and return to fixed positions in their environment. In returning to the starting point of their foraging excursion or to any other point, they could resort to two fundamentally different ways of navigation by using either egocentric or geocentric systems of reference. In the first case, they would rely on information continuously collected en route (path integration, dead reckoning), i.e. integrate all angles steered and all distances covered into a mean home vector. In the second case, they are expected, at least by some authors, to use a map-based system of navigation, i.e. to obtain positional information by virtue of the spatial position they occupy within a larger environmental framework. In bees and ants, path integration employing a skylight compass is the predominant mechanism of navigation, but geocentred landmark-based information is used as well. This information is obtained while the animal is deadreckoning and, hence, added to the vector course. For example, the image of the horizon skyline surrounding the nest entrance is retinotopically stored while the animal approaches the goal along its vector course. As shown in

Diverse theories of animal navigation aim at explaining how to determine and maintain a course from one place to another in the environment, although each presents a particular perspective with its own terminologies. These vocabularies sometimes overlap, but unfortunately with different meanings. This paper attempts to precisely define the existing concepts and terminologies, so as to comprehensively describe the different theories and models within the same unifying framework. We present navigation strategies within a 4 level hierarchical framework based upon levels of complexity of required processing (Guidance, Place recognition-triggered Response, Topological navigation, Metric navigation). This classification is based upon what information is perceived, represented and processed. It contrasts with common distinctions based upon availability of certain sensors or cues and rather stresses the information structure and content of central processors. We then review computat...

Navigation has always been an interdisciplinary topic of research, because mobile agents of different types are inevitably faced with similar navigational problems. Therefore, human navigation can readily be compared to navigation in other biological organisms or in artificial mobile agents like autonomous robots. One such navigational strategy, route-based navigation, in which an agent moves from one location to another by following a particular route, is the focus of this paper. Drawing on the research from cognitive psychology and linguistics, biology, and robotics, we present a simple, abstract formalism to express the key concepts of route-based navigation in a common scientific language. Starting with the distinction of places and route segments, we develop the notion of a route graph, which can serve as the basis for complex navigational knowledge. Implications and constraints of the model are discussed along the way, together with examples of different instantiations of parts of the model in different mobile agents. By providing this common conceptual framework, we hope to advance the interdisciplinary discussion of spatial navigation.

A taxonomy is described that relates different navigational behaviours in a hierarchical and compositional way. Elementary navigation tactics are combined to tactical navigation in routes; landmarks in space are contrasted to routemarks in networks of passages. Survey knowledge comes in at the level of strategic navigation. The Bremen Autonomous Wheelchair is then presented as a vehicle for experimentation in robotics, both to model biologically plausible navigational behaviours and to develop efficient navigational mechanisms for a technical application. The implementation on the autonomous system is based on the use of basic behaviours and the identification of routemarks. The actual recognition of artificial routemarks is described and early results of the current work on the identification of natural 3D-marks are presented. 1

The capacity to construct a cognitive map is hypothesized to rest on two foundations: (1) dead reckoning (path integration); (2) the perception of the direction and distance of terrain features relative to the animal. A map may be constructed by combining these two sources of positional information, with the result that the positions of all terrain features are represented in the coordinate framework used for dead reckoning. When animals need to become reoriented in a mapped space, results from rats and human toddlers indicate that they focus exclusively on the shape of the perceived environment, ignoring non-geometric features such as surface colors. As a result, in a rectangular space, they are misoriented half the time even when the two ends of the Behavioral and electrophysiological data suggest that mammals locate themselves and their goals on a cognitive

It is time to locate Connectionist representation theory in the new wave of robotics research. The utility of representations developed in Artificial Neural Networks during learning has been demonstrated in Cognitive Science research since the 1980s. The research reported here puts learned representations to work in a decentered control task, the disembodied arm problem, in which a mobile robot operates an arm fixed to a table to pick up objects. There is no physical linkage between the arm and the robot and so the robot's point of view must be decentered. This is done by developing a modular Artificial Neural Net system in three stages: (i) a Classifier net is trained with laser scan data; (ii) an Arm net is trained for picking up objects; (iii) an Inter net is trained to communicate and coordinate the sensing and acting. The completed system is shown to create new nonsymbolic transformationally invariant representations in order to perform the effective generalisation of decentered v...

Geometric inference is widely used in computer vision, but very little attention has been given to the question of how geometric properties affect the resulting errors in the inferences made. This thesis addresses the problem of the stability of geometric inference in determining locations with a goal of being able to predict type and magnitude of the errors which occur and to determine on what basis to make geometric inferences which will minimize error. It is shown that the amount of the error occurring in a localization process using angular measurements to features depends heavily on which features are used, that the amount of the error occurring in such a localization process is not a function of the number of features used, that it is possible to develop simple heuristic functions for choosing features for localization which will significantly decrease error in that localization, that it is possible to decrease localization error in a particular direction, and that, if features h...

For many centuries, philosophers and scientists have pondered the origins and nature of human intuitions about the properties of points, lines, and figures on the Euclidean plane, with most hypothesizing that a system of Euclidean concepts either is innate or is assembled by general learning processes. Recent research from cognitive and developmental psychology, cognitive anthropology, animal cognition, and cognitive neuroscience suggests a different view. Knowledge of geometry may be founded on at least two distinct, evolutionarily ancient, core cognitive systems for representing the shapes of large-scale, navigable surface layouts and of small-scale, movable forms and objects. Each of these systems applies to some but not all perceptible arrays and captures some but not all of the three fundamental Euclidean relationships of distance (or length), angle, and direction (or sense). Like natural number (Carey, 2009), Euclidean geometry may be constructed through the productive combination of representations from these core systems, through the use of uniquely human symbolic systems.

arks, in particular an image processing and navigation approach that has been studied primarily in social insects and birds. He models and extends this approach, uses it on two autonomous mobile systems in simulation and experiments, and thus demonstrates its robustness and practical use. Moreover, he realizes a complex tactical navigation algorithm for routes with artificial landmarks. Thomas Rofer has submitted a technically very demanding thesis that solves some theoretical and practical tasks in scene-based dynamic vision in a simple and elegant way; it also impresses by its interdisciplinary approach and the comprehensive treatment of its hardware and software realization. In particular, his contribution to the Bremen Autonomous Wheelchair is to be appreciated that has been successful in the DFG Focus Program "Spatial Cognition". On the basis of the work by Thomas Rofer and others we expect that the second prototype of the Bremen Autonomous Wheelchair, currently under development

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