Machine Learning for Swarm Exploration
01-29-03-ML-V
Dr. Dimtriy Shutin (DLR-IKN, Oberpfaffenhofen), Prof. Dr. Armin Dekorsy
This lecture introduces to the key and fundamental topics to understand swarm exploration. It also gives practical examples of applications where swarm exploration will be used in future such as seismological exploration tasks on Mars. Examples of content are:
- Why swarm exploration – some motivational examples with applications in space and on Earth
- Recap of probability and statistics (calculus of probabilities, moments, Bayesian theory)
- Machine learning tools (supervised learning, linear regression, kernel methods, neural networks, impact of regularization, sparsity and compressed sensing)
- Distributed machine learning and exploration (models for static spatial regression, information-theoretic exploration approaches, Bayesian sequential methods for learning and exploration)
- Discussion of several practical examples of swarm exploration solutions (cooperative localization, information-driven sparse mapping of magnetic fields, exploration of sparse gas sources using a swarm of mobile robots)