Wayne State researcher optimizing autonomous robot management with NSF-funded project
Humans receiving assistance from fleets of autonomous robots (AR) will soon become more commonplace and serve a variety of purposes in manufacturing, agriculture, health care, supply chain management and other industries. It has been estimated that the industrial robotics market will grow nearly 12% annually to over $33 billion in the next decade. A key driver of this projected growth is how easily AR fleets can be managed by end users.
Marco Brocanelli, assistant professor of computer science at Wayne State University, is investigating a system to more optimally coordinate and manage multi-purpose AR fleets to ensure continuity of operations and energy efficiency.
“The goal of this project is to achieve easy management of an AR fleet by allowing end users to indicate the set of tasks to execute during a working period and by implementing algorithms that automatically allocate tasks to ARs and coordinate recharge schedules,” said Brocanelli, who was recently awarded a two-year, $170,000 grant through the National Science Foundation’s Computer and Information Science and Engineering (CISE) Research Initiation Initiative (CRII). This prestigious, highly-competitive grant is awarded to early-career investigators to launch their research and academic careers.
Brocanelli’s autonomous fleet manager introduces a system that minimizes AR downtime and battery degradation. The system will coordinate sensors and computational resources to ensure an optimal ratio of energy consumption and task performance. A new hardware and software testbed will give Brocanelli and his students a means to study the AR fleet manager’s performance and scalability in real environments.
“The proposed work will introduce new technologies available for the management of AR fleets and boost the widespread adoption of autonomous robots in smart farms, smart cities, private companies and our homes,” said Brocanelli.
Data and results will be distributed on Brocanelli’s research website.
The grant number for this NSF award is 1948365.