Build and test various high level control and coordination techniques for autonomous robotic boats for monitoring water conditions in lakes and rivers. This is the main research focus of the INTCATCH H2020 project.
Possible ideas for projects in this area: i) intelligent exploration implementation and evaluation of approaches for intelligent water sampling; ii) HRI study of Human Robot Interaction approaches for controlling a team of autonomous boats;
Possible ideas: i) Situation Awareness analyse data (typically time-series) to understand what is the current situation for the drones. An example is to decide whether a water drone is moving upstream or downstream. ii) Planning under uncertainty find the best possible action given data received from sensors. An example is to decide the speed that a drone should maintain to make sure it will reach the end of a given path with enough battery. iii) Vision based situation awareness analyse data from cameras mounted on a drone to acquire high level information on the surrounding environment. An example is to detect the coastline for a water drone so to enchance autonomous navigation.
Intelligent veichles can be compromised by cyberattacks. An example is what happened to Chrysler in 2015, when it announced the recall of about 1.4m cars and trucks in the US due to vulnerabilities, which were exploited to get remote control of vehicles. Possible ideas for projects in this area: i) Study security problems related to malicious attacks that can alter the behaviour of the robot (e.g., reply attack, man-in-the-middle attack, and DoS attack). ii) Study potential risks related to the protection of the information and data gathered by a mobile robot and sent to a base station (or to the cloud).
Perform active malware analysis by using reinforcement learning approaches that interact with the malware in a sandbox. Design action selection strategies to learn malware models representing behaviors triggered by specific actions on the infected system. Compare learned models with machine learning approaches (e.g. clustering, classification etc...)
Implement and evaluate coordination approaches for Multi-robot systems. Possible ideas: i) design exploration and patrolling approaches for robotic agents (UAVs or UGVs). Test the solution on a widely used simulation environment (ROS + stage); ii) develop coordination strategies for heterogeneous agents (i.e., UAV + UGV) using team plan languanges