UAV Waypoint Opportunistic Navigation in GNSS-Denied Environments

Navigation of an unmanned aerial vehicle (UAV) to reach a desired waypoint with provable guarantees in global navigation satellite system (GNSS)-denied environments is considered. The UAV is assumed to have an unknown initial state (position, velocity, and time) and the environment is assumed to possess multiple terrestrial signals of opportunity (SOPs) transmitters with unknown states (position and time) and one anchor SOP whose states are known. The UAV makes pseudorange measurements to all SOPs to estimate its own states simultaneously with the states of the unknown SOPs. The waypoint navigation problem is formulated as a greedy (i.e., one-step look-ahead) multiobjective motion planning (MOMP) strategy, which guarantees that the UAV gets to within a user-specified distance of the waypoint with a user-specified confidence. The MOMP strategy balances two objectives: i) navigating to the waypoint; and (ii) reducing UAV's position estimate uncertainty. It is demonstrated that in such an environment, formulating the waypoint navigation problem in a so-called naive fashion by heading directly to the waypoint would result in failing to reach the waypoint. This is due to poor estimability of the environment. In contrast, the MOMP strategy guarantees (in a probabilistic sense) reaching the waypoint. Monte Carlo simulation results are presented showing that the MOMP strategy achieves the desired objective with 95% success rate compared to a 36% success rate with the naive approach. Experimental results are presented for a UAV navigating to a waypoint in a cellular SOP environment, where the MOMP strategy successfully reaches the waypoint, while the naive strategy fails to do so.

Automated summary

This study investigates the navigation of an unmanned aerial vehicle (UAV) to reach a desired waypoint in GNSS-denied environments. By employing a multiobjective motion planning (MOMP) strategy, the UAV can successfully reach the waypoint and reduce positioning uncertainty. Compared to the naive approach, the MOMP strategy achieves a higher success rate.