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Multirotor helicopters are very powerful flying robots used in many applications, but their lack is robustness. A fail in any of their rotors can drive the helicopter to fall. In this study two different micro-helicopter structures are analysed, a standard Quadrotor and a Hexrotor. An uncertainty model is generated and a robust controller is designed. The structured singular value μ is used to test the robust stability and performance of these two plants, obtaining better results for Hex than Quadrotor. At the end of the study, a set of flight tests, where some of the motors are partially damaged, is presented confirming the analysis of μ . Results of this study show Hexrotor as a very interesting structure for unmanned aerial vehiclesbecause of its stability and performance characteristics compared with Quadrotor.

The combined action of several sensing systems, so that they are able to compensate the technical flaws of each other, is common in robotics. Monte Carlo Localization (MCL) is a popular technique used to estimate the pose of a mobile robot, which allows the fusion of heterogeneous sensor data. Several sensor fusion schemes have been proposed which include sensors like GPS to improve the performance of this algorithm. In this paper, an Adaptive MCL algorithm is used to combine data from wheel odometry, an inertial measurement unit, a global positioning system and laser scanning. A particle weighting model which integrates GPS measurements is proposed, and its performance is compared with a particle generation approach. Experiments were conducted on a real robotic car within an urban environment.

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