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Unmanned aerial vehicle (UAV) is a remotely controlled and auto-piloted vehicle capable to sustaining lift due to aerodynamic forces. It finds its application in military for reconnaissance and airstrikes, carrying out search and rescue operations, terrain mapping and so on. To develop a robust and adaptive control system of a UAV a good mathematical model based on aerodynamic analysis is required...
A new control strategy based on feedback motion planning is presented for solving nonlinear control problems in constrained environments. The algorithm explores the state-space using a bi-directional rapidly exploring random tree (biRRT) in order to find a feasible trajectory between an initial and goal state. By incrementally scheduling LQR controllers, it attempts to connect states so as to link...
Focusing on the dynamic model parameter identification problem, this paper proposed an adaptive linear-time-domain system identification method for the micro unmanned helicopter robot. Based on the flash memory in the Micro Guide Navigation Control (MGNC), system recorded the flight data sequences regarding the input signal for servos and output signals for attitude and velocity information. Through...
We present a novel biomimetic approach to mapless autonomous navigation based on insect neuroethology. We implemented and tested a real-time neuronal model based on the Distributed Adaptive Control framework. The model unifies different aspects of insect navigation and foraging including landmark recognition, chemical search, path integration and optimal memory usage. Consistent with recent findings...
This paper presents a new approach which allows for the computation and optimization of feasible 3D flight trajectories within real time planning deadlines, for unmanned aerial systems (UAS) operating in environments with obstacles present. Sets of candidate flight trajectories have been generated through the application of maneuver automaton theory, where smooth trajectories are formed via the concatenation...
In continuation of our previous work on visual, appearance-based localization in manually built maps in this paper we present a novel appearance-based, visual SLAM approach. The essential contribution of this work is, an adaptive sensor model which is estimated online and a graph matching scheme to evaluate the likelihood of a given topological map. Both methods enable the combination of an appearance-based,...
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