Artificial perception techniques, allowing robot systems to know their location and surroundings using sensory data, have been instrumental for enabling robot automation outside of carefully controlled manufacturing settings. Current robot systems, however, remain passive in their perception of the world. Unlike biological systems, robots lack curiosity mechanisms for exploration and uncertainty mitigation, which are critical for intelligent decision making. Such capabilities are very important in disaster response, security and surveillance, and environmental monitoring, where it is necessary to quickly gain situational awareness of the terrain, buildings, and humans in the environment. The methods developed in this project will impact the design of mapping and active sensing algorithms for autonomous robot teams and their use in the aforementioned applications. This Faculty Early Career Development (CAREER) Program research develops fundamental robot autonomy capabilities that will also impact other domains relying on autonomous robots. In addition, the project will develop a suite of open-source education materials, including theoretical problems, projects, lectures, and exemplary implementations of core robotics algorithms, unified in an easily accessible simulation environment. This platform will support curriculum development for graduate students, as well as outreach and research-initiation activities for undergraduate and K-12 students.
The research agenda will be achieved through two key technical innovations. First, the project will formally define an Active Bayesian Inference problem, seeking optimal control of sensing systems for minimum uncertainty estimation. Methods for distributed approximate dynamic programming that utilize the structure of the problem, induced by the functions modeling probability mass evolution and estimation performance, will be developed to efficiently represent and optimize multi-robot sensing control policies. Second, the project will demonstrate that a team of ground and aerial robots, using Active Bayesian Inference techniques, can achieve autonomous exploration and active high-fidelity mapping of an unknown environment. This objective will be supported by novel contributions to online dense implicit surface mapping in terms of distributed and probabilistic techniques that allow multiple robots to collaboratively estimate the environment geometry and semantics, while quantifying the uncertainty of these estimates to allow planning informative actions.
This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).
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.
