Intelligent Cooperative Control for Urban Target Tracking with UAVs

Period of Performance: 09/18/2013 - 12/18/2015


Phase 2 SBIR

Recipient Firm

Utopiacompression, Corp.
11150 W. Olympic Blvd. Array
Los Angeles, CA 90064
Principal Investigator


ABSTRACT: Intelligent cooperative controller capable of continuous learning from offline and online experience is of sustained interest for a team of for unmanned aerial vehicles (UAVs) to execute complex missions in a dynamic environment. Current cooperative control technologies lack learning capability. Without the ability to learn, a cooperative system may not be able to react to unanticipated scenarios or adapt to dynamically changing environments in a correct and intelligent fashion, and therefore may lead to unsatisfactory system performance or even mission failure. In collaboration with Brigham Young University, UtopiaCompression Corporation proposed to develop an intelligent solution to target tracking in urban environment using multiple UAVs. During Phase I, we integrated a cooperative path planning algorithm with a machine learning algorithm into an intelligent target tracking controller. We successfully demonstrated the feasibility of improving target tracking performance through an online learning mechanism. We set up various baseline systems and illustrated that our proposed solution outperforms the baseline systems. In the Phase II effort, we will further mature our intelligent solution to target tracking and extend it to multi-target tracking scenario. We will enhance our simulation environment to better reflect realistic scenarios and demonstrate improved performance of our solution using Monte Carlo simulations. BENEFIT: In support of effective operations of unmanned aerial vehicles (UAVs) in increasingly complex and uncertain missions, the proposed technology will enable UAVs to learn and adapt to the uncertain environment and changes in adversary behavior. The solution will significantly increase the UAV-to-human ratio for successful operation reducing the overall deployment cost. The proposed learning framework will provide a team of UAVs with a set of tools to react effectively to changing environment, mission objectives and sensor characteristics. The new capabilities will allow the UAVs to complete the missions while flying safely, thus reducing costs due to possible accidents and mission failure. Within the commercial domain, the key technology areas and related applications that can potentially benefit from the proposed technology include surveillance around a critical or secure infrastructure, tracking of unknown targets and classifying their behaviors for border security, search of targets for rescue or surveillance, resupplying UAVs for aircraft carriers, Micro Air Vehicles and flying swarms for reconnaissance and remote monitoring, and civilian search and rescue. All of these applications require intelligent cooperation and decision making between UAVs, which will be enabled through our proposed hierarchical learning framework. UC has identified numerous product opportunities within the US Military modernization effort centering on implementing C4ISR (Command, Control, Computers, Communication, Intelligence, Surveillance, and Reconnaissance) technologies. ISR spending in the next decade is estimated at $15 billion, and depends largely on the stable and efficient control of unmanned vehicles. The UC team is particularly optimistic about the value of integrating UCs technology into the emerging smaller tactical UAVs under development and early deployment such as the RQ-7 and RQ-11 programs. Other potential application programs include the Predator UAV programs, as well as multiple FAA compliance efforts currently underway.