This paper reviews the effect of multirotor aerial vehicle designs on their abilities in terms of tasks and system properties. We propose a general taxonomy to characterize and describe multirotor aerial vehicles and their designs, which we apply exhaustively on the vast literature available. Thanks to the systematic characterization of the designs, we exhibit groups of designs having the same abilities in terms of achievable tasks and system properties. In particular, we organize the literature review based on the number of atomic actuation units and we discuss global properties arising from their choice and spatial distribution in the mechanical designs. Finally, we provide a discussion on the common traits of the designs found in the literature and the main open and future problems.

The aim of this paper is to present the design of a novel omnidirectional Unmanned Aerial Vehicle (UAV) with seven uni-directional thrusters, called O 7 +. The paper formally defines the O + design for a generic number of propellers and presents its necessary conditions; then it illustrates a method to optimize the placement and orientation of the platform's propellers to achieve a balanced O + design. The paper then details the choice of the parameters of the O 7 + UAV, and highlights the required mechanical and electrical components. The resultant platform is tested in simulation, before being implemented as a prototype. The prototype is firstly static-bench tested to match its nominal and physical models, followed by hovering tests in multiple orientations. The presented prototype shows the ability to fly horizontally, upside down and at a tilted angle.

In this paper we propose to control a quadrotor through direct acceleration feedback. The proposed method, while simple in form, alleviates the need for accurate estimation of platform parameters such as mass and propeller effectiveness. In order to use efficaciously the noisy acceleration measurements in direct feedback, we propose a novel regressionbased filter that exploits the knowledge on the commanded propeller speeds, and extracts smooth platform acceleration with minimal delay. Our tests show that the controller exhibits a few millimeter error when performing real world tasks with fast changing mass and effectiveness, e.g., in pick and place operation and in turbulent conditions. Finally, we benchmark the direct acceleration controller against the PID strategy and show the clear advantage of using high-frequency and low-latency acceleration measurements directly in the control feedback, especially in the case of low frequency position measurements that are typical for real outdoor conditions.

We present a novel human-aware navigation approach, where the robot learns to mimic humans to navigate safely in crowds. The presented model, referred to as Deep-MoTIon, is trained with pedestrian surveillance data to predict human velocity in the environment. The robot processes LiDAR scans via the trained network to navigate to the target location. We conduct extensive experiments to assess the components of our network and prove their necessity to imitate humans. Our experiments show that DeepMoTIion outperforms all the benchmarks in terms of human imitation, achieving a 24% reduction in time series-based path deviation over the next best approach. In addition, while many other approaches often failed to reach the target, our method reached the target in 100% of the test cases while complying with social norms and ensuring human safety.

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This letter shows for the first time why it is important and how to optimize the gains of a position controller on board of a fully-actuated aerial vehicle with bounded lateral force, via an auto-tuning approach. In such vehicles, most of the control authority is expressed along a principal thrust direction, while along lateral directions smaller forces can be exploited to achieve full-pose tracking. The nonlinear and hard to model interplay between the constraint imposed on the lateral force and the gains of the position controller is overcome by employing the OPTIM-tune calibration method. Several experimental tests, performed fully autonomously during flight, clearly show the practicability and benefits of the approach.

This paper presents a theoretical study on the ability of multi-rotor aerial vehicles (MRAVs) with tiltable propellers to achieve and sustain static hovering at different orientations. To analyze the ability of MRAVs with tiltable propellers to achieve static hovering, a novel linear map between the platform's control inputs and applied forces and moments is introduced. The relation between the introduced map and the platform's ability to hover at different orientations is developed. Correspondingly, the conditions for MRAVs with tiltable propellers to realize and sustain static hovering are detailed. A numerical metric is then introduced, which reflects the ability of MRAVs to sustain static hovering at different orientations. A subclass of MRAVs with tiltable propellers is defined as the Critically Statically Hoverable platforms (CSH), where CSH platforms are MRAVs that cannot sustain static hovering with fixed propellers, but can achieve static hovering with tilting propellers. Finally, extensive simulations are conducted to test and validate the above findings, and to demonstrate the effect of the proposed numerical metric on the platform's dynamics.

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