We present an error tolerant path planning algorithm for Micro Aerial Vehicles (MAV) swarms. We assume a MAV navigation system without relying on GPS-like techniques. The MAV find their navigation path by using their sensors and cameras, in order to identify and follow a series of visual landmarks. The visual landmarks lead the MAV towards the target destination. MAVs are assumed to be unaware of the terrain and locations of the landmarks. Landmarks are also assumed to hold a-priori information, whose interpretation (by the MAVs) is prone to errors. We distinguish two types of errors, namely, recognition and advice. Recognition errors are due to misinterpretation of sensed data and a-priori information or confusion of objects (e.g., due to faulty sensors). Advice errors are due to outdated or wrong information associated to the landmarks (e.g., due to weather conditions). Our path planning algorithm proposes swarm cooperation. MAVs communicate and exchange information wirelessly, to minimize the recognition and advice error ratios. By doing this, the navigation system experiences a quality amplification in terms of error reduction. As a result, our solution successfully provides an adaptive error tolerant navigation system. Quality amplification is parametetrized with regard to the number of MAVs. We validate our approach with theoretical proofs and numeric simulations.
The resulting classifiers had been tested via experimental work. * mavs with high-definition cameras, recording images at a resolution of @math x @math pixels, at the speed of @math fps (frames per second) are loaded a given classifier, to evaluate a visual classification ratio. Each experiment consists of building a classifier and getting the averaged ratio. Results by in @cite_14 @cite_3 show an average empirical visual error ratio of about @math the landmarks, on average). The results are compared to some other well-established pattern recognition methods for the visual identification of objects, such as minimum distance and @math -nearest neighbor classification algorithms.
@cite_14 @cite_3
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Indoor navigation of an unmanned aerial vehicle is the topic of this article. A dual feedforward feedback architecture has been used as the UAV´s controller and the K-NN classifier using the gray level image histogram as discriminant variables has been applied for landmarks recognition. After a brief description of the aerial vehicle we identify the two main components of its autonomous navigation, namely, the landmark recognition and the controller. Afterwards, the paper describes the experimental setup and discusses the experimental results centered mainly on the basic UAV´s behavior of landmark approximation which in topological navigation is known as the beaconing or homing problem. In this paper, the fusion of probabilistic knowledge-based classification rules and learning automata theory is proposed and as a result we present a set of probabilistic classification rules with self-learning capability. The probabilities of the classification rules change dynamically guided by a supervised reinforcement process aimed at obtaining an optimum classification accuracy. This novel classifier is applied to the automatic recognition of digital images corresponding to visual landmarks for the autonomous navigation of an unmanned aerial vehicle (UAV) developed by the authors. The classification accuracy of the proposed classifier and its comparison with well-established pattern recognition methods is finally reported.