The course is mainly divided into three parts:
Part I: Elements of robotics. The basic elements of Robotics are explained by referring to the manipulator, i.e., the
kinematics along with the Denavit-Hartenberg parameters and the homogeneous roto-translation representation, the
differential kinematics, the statics, and the dynamics. Moreover, the trajectory planning will be considered by using
both traditional methods and advanced methods based on meta-heuristic optimization (e.g., the Particle Swarm
Optimization). All the previous elements will be used to introduce some basic control algorithms.
Part II: Elements of Artificial Intelligence and Machine Learning. The basic elements of artificial intelligence and
machine learning are explained, and examples related both to robotics and space exploration will be considered.
Specifically, some basic elements for dealing with collection and pre-processing of data will be discussed. Then, simple
algorithms from machine learning will be addressed, such as the random forest or the support vector machines.
Convolutional neural networks will be described, also taking into account the possibility to put such algorithm on-board
for autonomous satellites. Innovative recognition and "detection" algorithms on neural networks and/or on features
extraction and latest generation matching techniques on EO / IR and SAR images. Examples dealing with remote
sensing and space exploration will be shown. Finally, GAN architectures will be presented.
Part III: New algorithms for navigation of space systems based on AI. Starting from optical and infrared mavigation,
sensor errors, as aberration, boresight, noise input, will be discussed, in order to explain elements of optical and infrared
tracking systems (e.g., missile seekers). Finally, AI image enhancement algorithms to increase the performance of an
electro-optical sensor and algorithms for super-resolving the image / object with single will be described.
