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Systems and control theory for multi-robot control
PI: Kaoru Yamamoto, Andreas Themelis
A system is a group of interacting elements and the aim of “control” is to achieve the desired system behaviour. From physical systems such as robots, drones, and vehicles to signal processing such as music signals and images, anything with dynamics can be a candidate for the systems to be controlled. Our group studies systems & control theory and various applications in this area. In particular, we work on autonomous vehicles/mobile robots such as vehicle platooning and cooperative drones. Each agent in such a system can only collect some partial information in the network such as the position and/or speed of the neighbouring agents, and our task is to control the behaviour of the whole network under such constraints.
High-precision Control in Sampled-data/Cyber-physical systems
PI: Kaoru Yamamoto
Systems consisting of a plant (a system to be controlled) operating in continuous time and a digital controller (e.g., computer control) are called “sampled-data systems”. Many real-world systems, such as robotic control systems, fall into this category. Cyber-physical systems are also sampled-data systems, as they involve controlling physical plants using computational resources in cyberspace. In such systems, the states of the plants are typically measured at discrete sampling times, and the dynamics between sampling points are often neglected in conventional controller design methods.
Optimization
PI: Andreas Themelis
The increase in computing capability and the development of powerful (micro)processors that we have witnessed in the last years has motivated engineers to design more sophisticated ad-hoc control strategies based on "Optimization". The importance of this science is dictated by the fact that virtually any engineering problem is (or can be reduced to) a functional minimization. For instance, finding the “best” route in path planning amounts to finding the one that minimizes a cost (function), which is the contribution of factors such as distance, time, fuel consumption, and so on. The main challenge is to find a suitable balance between convergence speed, low computational requirements, and the range of problems that can be solved. Our group aims to develop efficient algorithms to be employed in a wide range of engineering applications, including, but not limited to, control and signal processing.
Transformable Drone
PI: Akinori Sakaguchi
Drones (multirotors) have a simple structure with multiple rotors arranged in a radial pattern, but they have high maneuverability (hover and move up, down, left, right, forward, and backward) by controlling each rotor speed. However, since it has only 4 DoF (Degree of Freedom) compared to 6 DoF motion (3 DoF translational motion + 3 DoF rotational motion), it is limited to translational motion without tilting the attitude. In order to improve the degree of freedom of the aircraft, we are developing an original "transformable drone" that incorporates a transformable mechanism (tilting frame mechanism) in the frame, which has been fixed in conventional drones, and designing a controller that enables stable flight even when the shape is greatly deformed. By using the ability to transform the frame, we aim to efficiently accomplish complex tasks that conventional drones cannot, such as gathering information at a narrow disaster site.
Transformable drones with tilting mechanisms
We proposed a novel quadrotor with a tilting frame using the parallel link mechanism. By driving only one servo motor, it can tilt its frame in the pitch direction, that is, it has 5 DoF (Degree of Freedom), so that its frame is folded vertically.
Transformable drones with tilting mechanisms
We proposed a novel quadrotor with a dual-axis tilting frame using the parallelepiped mechanism. By driving only two servo motors, it can tilt its frame in the roll and the pitch direction, that is, it has 6 DoF (Degree of Freedom), so that its frame is folded vertically in those two directions.