Distributed Joint Localization and Tracking for Multi-robot Networks Under Local Sensing and Communication Constraints with Theoretical Guarantees

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This Dynamics, Control, and System Diagnostics project will support research to enable large-scale, distributed coordination of multi-robot systems, promoting the progress of science and advancing the national prosperity. In applications such as surveillance, security, and disaster response, a network of mobile autonomous robots collectively tracks a subject over a wide area. As the subject and the robots move in and out of observation and communication range, each robot in the network needs to propagate estimates of position, velocity, and orientation for both itself and the subject, based on noisy measurements. The first task is called "localization," and the second task is called "tracking." Communication limitations mean that it is often infeasible to rely on a central controller, so each robot must be responsible for making its own decisions. This project will address the problem of how to coordinate information among the individual robots, to allow each of them to make the best possible decisions. Results from the project will benefit numerous applications involving multi-robot systems such as surveillance and security, disaster response and environmental monitoring. Education and outreach activities will broaden participation of underrepresented groups in research. The objective of this project is to address the fundamental theoretical and practical issues caused by sensing gaps, local communication, and lack of absolute pose measurements in distributed collaborative sensing with mobile robotic networks. The first thrust is to derive a unified framework for fully distributed joint localization and target tracking through local cooperation. The second thrust is rigorous theoretical analysis of the framework with realistic issues, namely, sparse and changing sensing and communication and uncertain target models. The third thrust is experimental demonstration on mobile robotic networks with physical constraints. The research will significantly advance distributed estimation in both theory and applications.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.