K. Weber

Bio: K. Weber is an academic researcher from Curtin University. The author has contributed to research in topics: Mobile robot & Robot. The author has an hindex of 7, co-authored 12 publications receiving 594 citations.

Robot navigation inspired by principles of insect vision

Abstract: Recent studies of insect visual behaviour and navigation reveal a number of elegant strategies that can be profitably applied to the design of autonomous robots. The “peering” behaviour of grasshoppers, for example, has inspired the design of new rangefinding systems. The “centring” response of bees flying through a tunnel has led to simple methods for navigating through corridors. These and other visually-mediated insect behaviours are described along with a number of applications to robot navigation.

Insect inspired behaviours for the autonomous control of mobile robots

Abstract: Animals navigate through various uncontrolled environments with seemingly little effort. Flying insects, especially, are quite adept at manoeuvring in complex, unpredictable and possibly hostile environments. Through both simulation and real-world experiments, we demonstrate the feasibility of equipping a mobile robot with the ability to navigate a corridor environment, in real time, using principles based on insect-based visual guidance. In particular we have used the bees' navigational strategy of measuring object range in terms of image velocity. We have also shown the viability and usefulness of various other insect behaviours: 1) keeping walls equidistant, 2) slowing down when approaching an object, 3) regulating speed according to tunnel width, and 4) using visual motion as a measure of the distance travelled.

Insect-inspired robotic homing

Abstract: Many animals, including insects, successfully engage in visual homing. We describe a system that allows a mobile robot to home. Specifically, we propose a simple, yet robust, homing scheme that only relies upon the observation of the bearings of visible landmarks. However, this can easily be extended to include other visual cues. The homing algorithm allows a mobile robot to home incrementally by moving in such a way as to gradually reduce the discrepancy between the current view and the view obtained from the home position. Both simulation and mobile robot experi ments are used to demonstrate the feasibility of the approach.

Insect inspired behaviours for the autonomous control of mobile robots

Abstract: Animals navigate through various uncontrolled environments with seemingly little effort. Flying insects, especially, are quite adept at manoeuvring in complex, unpredictable and possibly hostile environments. Through both simulation and real-world experiments, we demonstrate the feasibility of equipping a mobile robot with the ability to navigate a corridor environment, in real time, using principles based on insect-based visual guidance. In particular we have used the bees' navigational strategy of measuring object range in terms of image velocity. We have also shown the viability and usefulness of various other insect behaviours: 1) keeping walls equidistant, 2) slowing down when approaching an object, 3) regulating speed according to tunnel width, and 4) using visual motion as a measure of the distance travelled.

Evolutionary robotics: The biology, intelligence, and technology of self‐organizing machines

Abstract: Let's read! We will often find out this sentence everywhere. When still being a kid, mom used to order us to always read, so did the teacher. Some books are fully read in a week and we need the obligation to support reading. What about now? Do you still love reading? Is reading only for you who have obligation? Absolutely not! We here offer you a new book enPDFd evolutionary robotics the biology intelligence and technology of self organizing machines intelligent robotics and autonomous agents series to read.

Flapping flight for biomimetic robotic insects: part I-system modeling

Abstract: This paper presents the mathematical modeling of flapping flight inch-size micro aerial vehicles (MAVs), namely micromechanical flying insects (MFIs). The target robotic insects are electromechanical devices propelled by a pair of independent flapping wings to achieve sustained autonomous flight, thereby mimicking real insects. In this paper, we describe the system dynamic models which include several elements that are substantially different from those present in fixed or rotary wing MAVs. These models include the wing-thorax dynamics, the flapping flight aerodynamics at a low Reynolds number regime, the body dynamics, and the biomimetic sensory system consisting of ocelli, halteres, magnetic compass, and optical flow sensors. The mathematical models are developed based on biological principles, analytical models, and experimental data. They are presented in the Virtual Insect Flight Simulator (VIFS) and are integrated together to give a realistic simulation for MFI and insect flight. VIFS is a software tool intended for modeling flapping flight mechanisms and for testing and evaluating the performance of different flight control algorithms

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.