Deployable, Data-Driven Unmanned Vehicle Navigation System in GPS-Denied, Feature-Deficient Environments
This addresses the challenge of reliable navigation for unmanned vehicles in constrained environments such as mines or tunnels, representing an incremental improvement by combining existing techniques in a novel way.
The paper tackles the problem of navigating unmanned vehicles in GPS-denied, feature-deficient environments like tunnels by developing a data-driven system that uses deployable landmarks for localization, achieving a desired maximum position uncertainty with a minimized number of landmarks.
This paper presents a novel data-driven navigation system to navigate an Unmanned Vehicle (UV) in GPS-denied, feature-deficient environments such as tunnels, or mines. The method utilizes landmarks that vehicle can deploy and measure range from to enable localization as the vehicle traverses its pre-defined path through the tunnel. A key question that arises in such scenario is to estimate and reduce the number of landmarks that needs to be deployed for localization before the start of the mission, given some information about the environment. The main focus is to keep the maximum position uncertainty at a desired value. In this article, we develop a novel vehicle navigation system in GPS-denied, feature-deficient environment by combining techniques from estimation, machine learning, and mixed-integer convex optimization. This article develops a novel, systematic method to perform localization and navigate the UV through the environment with minimum number of landmarks while maintaining desired localization accuracy. We also present extensive simulation experiments on different scenarios that corroborate the effectiveness of the proposed navigation system.