Recently, I started working on Model Predictive Control (MPC) applied to multi-rotor aerial systems with arbitrarily positioned and oriented rotors as a high-performance constrained and predictive control technique. Also, I'm focusing on Temporal Logic, specifically Signal Temporal Logic (STL), as a mission specification language for path planning problems. Such technique allows incorporating explicit timing constraints and translate the whole problem in mixed integer-linear constraints on the system variables.
Aerial robotics is a fast-growing field of robotics and in particular multi-rotor aircraft, like quadrotors, are rapidly increasing in popularity also out of the scientific community. However, designing autopilots for UAVs (Unmanned Aerial Vehicles) is a challenging task, which involves multiple interconnected aspects. Therefore, having tools able to show what it happens when some new applications are going to be developed in unknown or critical situations is more and more important.
Simulation is one of such helpful tools, widely used in robotics, enabling not only to verify the components integration and to evaluate their behavior under different circumstances but also to simplify the development and validation processes. Furthermore, simulation is cheaper than experiments with real robots, in terms of time and human resources: it makes possible simulating multiple robots when the hardware may not be available and getting a better understanding of implemented methods under various conditions.
The aim of my research was to illustrate how the software-in-the-loop (SIL) methodology allows to detect and manage instabilities of a multi-rotor aircraft system that otherwise might not arise when considering only MATLAB/Simulink simulations. The use of the SIL technique allows to understand the behavior of the flight control system by comparing and evaluating different scenarios, with a details level quite close to reality. At the same time, it is possible to discover issues that a model-in-the-loop (MIL) simulation does not necessarily detect, even if carried out through a multi-physics co-simulation approach.
The research proposes a co-design approach based on SIL methodologies for performance evaluation, designing and developing algorithms for autonomous systems. The simulation scheme consists of specific middleware, namely ROS (Robot Operating System), where one node or computer simulates the control unit (e.g., MATLAB/Simulink equipped with Robotics System Toolbox), and another one the vehicles in the scenario (sensor models and obstacles are simulated, too), through 3D simulation environments (e.g., Gazebo, V-REP), with the aim of implementing the overall control system together with the flight dynamics and the environment.
Although advantages of such methodology are reasonable for the scientific community from a very general viewpoint, illustrative case studies and a complete software methodology can be of interest in particular if declined to the specific application, and when the code is provided as open-source for scientific and educational activities
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