Project: #14
Learning-based control of delayed robotic networks subject to disturbances
One of the main objectives of the research is to enable formation control in general robotic networks, which can model physical systems with higher-order robot dynamics in the presence of delays and disturbances. Such delays and disturbances are common in robotic systems and can occur either due to time lost during sensing or communication between neighboring agents, or due to local agent dynamics, as well as the interaction between the agents and the working environment. Learning-based control that intelligently harnesses information about the delays and disturbances is proposed to maintain stability in higher-order delayed and disturbed agents. This informs the subsequent proposed objectives of improving cooperative transport of objects using distributed robotic networks. This research project has applications in warehouse automation and flexible manufacturing using robots for material handling operations.
One of the main objectives of the research is to enable formation control in general robotic networks, which can model physical systems with higher-order robot dynamics in the presence of delays and disturbances. Such delays and disturbances are common in robotic systems and can occur either due to time lost during sensing or communication between neighboring agents, or due to local agent dynamics, as well as the interaction between the agents and the working environment. Learning-based control that intelligently harnesses information about the delays and disturbances is proposed to maintain stability in higher-order delayed and disturbed agents. This informs the subsequent proposed objectives of improving cooperative transport of objects using distributed robotic networks. This research project has applications in warehouse automation and flexible manufacturing using robots for material handling operations.