The proposed work focuses on developing resource-adaptive distributed estimation algorithms for teams of micro aerial vehicles (MAVs). Among the several challenges one faces when designing estimators for MAV teams, the most important one is the stringent resource limitations of MAVs. While in any engineered system the resources are inevitably finite, cost, power, and weight considerations make the limitations particularly strict for teams of small MAVs. In this work, we will develop a rigorous, optimization-based framework for the design of estimation and inference algorithms, as well as for the design of the MAV platforms themselves. Our approach will yield distributed estimators capable of optimally allocating the sensing, processing, communication, and energy resources of an MAV team. The methods to be developed will lead to efficient design tools, and will permit a systematic study of the tradeoff curves between MAV resource availability and estimation performance.
The algorithms and theoretical results that will result from this effort will dramatically increase the capabilities of MAV teams, in domains ranging from scientific exploration to search-and-rescue operations. In turn, these systems will yield benefits that will directly impact our lives, from advancing our state of scientific understanding to saving humans in disaster sites. Additionally, the proposed research plan will create opportunities for both graduate and undergraduate students from UC Riverside's diverse student body to conduct meaningful research. Undergraduate students will be recruited to work on new MAV designs, and it is anticipated that such an involvement will increase the likelihood of them pursuing a graduate education. Moreover, as part of an integrated outreach program, we will leverage the nature of the proposed research (flying robots capture the imagination of young minds) to inspire and recruit underrepresented minority students to science and engineering. These efforts will aid in closing the educational attainment gap for underrepresented groups.
