Today small autonomous helicopters offer a low budget platform for aerial applications such as surveillance (both military and civilian), land management, and earth science. In this paper we introduce a prototype of autonomous aerial vehicle, the Helibot helicopter, specifically designed for applications in a cooperative network based on autonomous agents as UAV and UGV. We present our scalable and robust architecture, focusing in particular on hardware and real time solutions. A mechanical structure for safe testing is then presented.
Autonomous robots require many types of information to obtain intelligent and safe behaviours. For outdoor operations, semantic mapping is essential and this paper proposes a stochastic automaton to localise the robot within the semantic map. For correct modelling and classification under uncertainty, this paper suggests quantising robotic perceptual features, according to a probabilistic description, and then optimising the quantisation. The proposed method is compared with other state-of-the-art techniques that can assess the confidence of their classification. Data recorded on an autonomous agricultural robot are used for verification and the new method is shown to compare very favourably with existing ones.
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