Cooperative Relative Navigation of Multi-agent Systems (CRANES)

In light of technological advances, the past decade has seen a rise of multi-agent systems (or swarms) in various sectors, including aerospace, robotics, automotive and aviation to name a few. These networked multi-agent systems are tasked to execute missions, which were previously only feasible by single-agent monolithic systems. In the field of aerospace, single satellites are being replaced with satellite swarms, e.g., the StarLink network to broaden service coverage. Along similar lines, single dish space-based interferometry will be replaced by a satellite swarm (e.g., OLFAR project) for space-based radio astronomy, to improve angular resolution of the cosmic images. 

Knowledge of position, timing, and orientation (PTO) is vital information for the healthy operation of any mobile network. Furthermore, it is imperative that any data collected and processed during the mission lifetime be stamped with the PTO information, for prudent inference during post-processing. To this end, this project aims to solve the challenges of multi-target PTO tracking in a mobile network, with intermittent or no external information. The agents in the network must dynamically estimate both their individual PTO, and cooperatively estimate the PTO of their fellow agents in the network in the absence of a centralized master. In this project, distributed robust Bayesian algorithms for relative navigation will be developed to avoid single-point-of-failure and to minimize processing and communication resources of the agents for practical implementation. The proposed solutions will be scalable for larger networks, and robust against sending, processing and communication errors.