Large networks of electromechanical agents, including complex robotic manipulators and self-driving cars, will soon become ubiquitous. Advanced control of such systems will increasingly benefit from inexpensive solid-state cameras but the resulting vision-based systems will be computation-intensive and introduce significant delays. Delays are known to cause instability in control systems, leading potentially to catastrophic failures in critical infrastructure. This project will develop methods to compensate and overcome such delays in networked control systems. The project will also contribute to the development of human resources in science and technology, continuing the efforts of the investigators who have supervised two dozen students from underrepresented groups on past projects, including women, Hispanics, and African-Americans.
The methods developed in the project are tailored to the efficient use of image data to facilitate real-time multi-agent path planning and collision avoidance. This will be accomplished using a combination of geometric representations and feature-based learning. Orientations and 3-D positions will be characterized for HSV color-classified objects to be grasped, for obstacles, and for destinations where the objects are to be placed. These orientations and 3-D positions will be employed for real-time generation of collision-free paths to be used as desirable trajectories. Computationally efficient fully decentralized and energy-efficient extremum seeking control schemes will be formulated, leveraging inherently parallel and highly redundant processing architecture and operating in the presence of interconnected nonlinearities, saturations, and uncertainties. Experimental validation of the results, using a 28th-order Baxter robot, will be carried out to examine the robustness and to validate the computational-energy efficiency of the algorithms.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
