Student projects

A neural network classifier is fragile in the sense that a small perturbation to its input can lead to a wrong classification. Thus, an attacker can leverage this fragility to perturb an input. For example, in autonomous driving, adding small noise to a Stop Sign can change the Sign to a Yield (see figure). If (…)

Reinforcement learning addresses finding a policy for a Markov decision process (MDP) to optimise a cumulative reward function based on the observations of the rewards and the evolution of the MDP. The application of reinforcement learning to safety-critical systems such as autonomous driving and robotics requires safety, that is, satisfying constraints while learning (e.g. collision (…)

In a constrained optimization problem arising in practice, the objective or constraint functions depend on model parameters. These parameters are often learned from data. The learning procedure however can only estimate the true parameters. It can be shown that using nominal estimated values of the parameters can lead to solutions of the optimization problem that (…)

 Decision making in multi-agent systems arises in applications ranging from online auctions and markets to telecommunication and transportation networks. Game theory provides a powerful framework for analyzing and optimizing decisions in multi-agent systems. The notion of an equilibrium in a game characterizes stable solutions to multi-agent decision making problems.The objective of this project is to (…)

Multi-robot systems provide great benefits in applications such as coordinated search and rescue, large scale agriculture, and efficient transportation of people and goods. This motivates research on novel coordination, planning, and control algorithms. To develop such algorithms it is crucial to test ideas rapidly in a setting that is both repeatable and adaptable to different (…)

Multi-robot systems are becoming increasingly popular and are being used in more and more applications such as warehouses, agriculture, and transportation. The objective of this project is to control and coordinate a group of ground robots. Controlling a single ground robot requires planning the desired path and computing actuation inputs to maneuver the robot based (…)

Multi-robot systems provide great benefits in applications such as coordinated search and rescue, large scale agriculture, and efficient transportation of people and goods. When a group of mobile robots operate in shared space coordination between them is crucial. In particular, strategies for task assignment and collision avoidance are needed. Task assignment is the problem of (…)

Motion planning plays a key role in the autonomous control of mobile robots. With the advancement of technology and the introduction of increasingly sophisticated autonomous vehicles, such as self-driving cars, delivery drones, and warehouse robots, complex motion planning objectives and constraints need to be considered. The objective for an autonomous car could be the minimisation (…)