Marinus Maris

Bio: Marinus Maris is an academic researcher from University of Zurich. The author has contributed to research in topics: Mobile robot & Compass. The author has an hindex of 5, co-authored 6 publications receiving 240 citations.

An autonomous agent navigating with a polarized light compass

Abstract: One of the fundamental abilities required in autonomous agents is homing. Natural agents—for instance, desert ants—solve the homing problem mainly by using path integration within an egocentric frame of reference. When employing such a mechanism, compass information for determining direction is necessary, and the precision of the compass will have a crucial effect on the precision of homing. For deriving compass information, certain insects use the pattern of polarized light in the sky that arises due to scattering of sunlight in the atmosphere (polarized light compass). The analysis of skylight polarization is mediated by specialized photoreceptors and neurons in the visual system. Inspired by the insect's polarized light compass, we have constructed a polarization compass that was employed successfully on the mobile robot Sahabot. Three models for extracting compass information from the polarization pattern of the sky were tested. In this article, we describe the navigation system and report results of ...

Navigating with a polarized light compass

Abstract: The aim of this study was to implement in an artificial autonomous agent biological mechanisms of polarized light navigation and to test in the field whether the existing models of signal processing are suitable to guide the robot on a simple round trip in a straight line. It seems that our attempts have been successful in that the precision of robot navigation is comparable to that of the desert ant, Cataglyphis fortis.

Cheap designs: Exploiting the dynamics of the system-environment interaction. Three case studies on navigation

Abstract: Examples of prerational intelligence are given using three case studies of robot navigation. It is demonstrated that the philosophy of focusing on cheap designs contributes significantly to our understanding of behavior. The real world has properties which can be exploited in the agent-environment interaction. This interaction, which is based on physical rather than information processes, must be taken into account when designing agents. It is also an essential part of understanding animal and human behavior.

A strong winner-take-all neural network in analogue hardware

Abstract: A winner-take-all model is presented that is suited for an implementation in an analogue circuit. The model is strong and value-preserving and requires only O(n) connections. Existence of a single equilibrium state and convergence is proved. The operation is demonstrated for a test circuit with four neurons built from discrete components and standard op-amps. Furthermore, a possible aVLSI implementation is derived and tested in a simulation.

Developmental robotics: a survey

Abstract: Developmental robotics is an emerging field located at the intersection of robotics, cognitive science and developmental sciences. This paper elucidates the main reasons and key motivations behind the convergence of fields with seemingly disparate interests, and shows why developmental robotics might prove to be beneficial for all fields involved. The methodology advocated is synthetic and two-pronged: on the one hand, it employs robots to instantiate models originating from developmental sciences; on the other hand, it aims to develop better robotic systems by exploiting insights gained from studies on ontogenetic development. This paper gives a survey of the relevant research issues and points to some future research directions.

Can robots make good models of biological behaviour

Abstract: How should biological behaviour be modelled? A relatively new approach is to investigate problems in neuroethology by building physical robot models of biological sensorimotor systems. The explication and justification of this approach are here placed within a framework for describing and comparing models in the behavioural and biological sciences. First, simulation models – the representation of a hypothesis about a target system – are distinguished from several other relationships also termed “modelling” in discussions of scientific explanation. Seven dimensions on which simulation models can differ are defined and distinctions between them discussed:1. Relevance: whether the model tests and generates hypotheses applicable to biology.2. Level: the elemental units of the model in the hierarchy from atoms to societies.3. Generality: the range of biological systems the model can represent.4. Abstraction: the complexity, relative to the target, or amount of detail included in the model.5. Structural accuracy: how well the model represents the actual mechanisms underlying the behaviour.6. Performance match: to what extent the model behaviour matches the target behaviour.7. Medium: the physical basis by which the model is implemented.No specific position in the space of models thus defined is the only correct one, but a good modelling methodology should be explicit about its position and the justification for that position. It is argued that in building robot models biological relevance is more effective than loose biological inspiration; multiple levels can be integrated; that generality cannot be assumed but might emerge from studying specific instances; abstraction is better done by simplification than idealisation; accuracy can be approached through iterations of complete systems; that the model should be able to match and predict target behaviour; and that a physical medium can have significant advantages. These arguments reflect the view that biological behaviour needs to be studied and modelled in context, that is, in terms of the real problems faced by real animals in real environments.

Where did I take that snapshot? Scene-based homing by image matching

Abstract: In homing tasks, the goal is often not marked by visible objects but must be inferred from the spatial relation to the visual cues in the surrounding scene. The exact computation of the goal direction would require knowledge about the distances to visible landmarks, information, which is not directly available to passive vision systems. However, if prior assumptions about typical distance distributions are used, a snapshot taken at the goal suffices to compute the goal direction from the current view. We show that most existing approaches to scene-based homing implicitly assume an isotropic landmark distribution. As an alternative, we propose a homing scheme that uses parameterized displacement fields. These are obtained from an approximation that incorporates prior knowledge about perspective distortions of the visual environment. A mathematical analysis proves that both approximations do not prevent the schemes from approaching the goal with arbitrary accuracy, but lead to different errors in the computed goal direction. Mobile robot experiments are used to test the theoretical predictions and to demonstrate the practical feasibility of the new approach.