We explore planning algorithms for Micro Aerial Vehicles (MAVs) and MAV swarms. We assume a MAV navigation system without relying on GPS-like techniques. The MAVs 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 MAVs 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 Advice errors are due to outdated or wrong information associated to the landmarks (e.g., due to weather conditions).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, using a Java simulator. Our simulations explore the complexity and impact of each of those communication models in terms of swarm intelligence.
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