Efficient path-following is crucial in most of the applications of autonomous vehicles (UxV). Among various guidance strategies presented in literature, the look-ahead distance ($L_1$)-based nonlinear guidance has received significant attention due to its ease in implementation and ability to maintain a low cross-track error while following simpler reference paths and generating bounded lateral acceleration commands. However, the constant value of $L_1$ becomes problematic when the UxV is far away from the reference path and also produces higher cross-track error while following complex reference paths having high variation in radius of curvature. To address these challenges, the notion of look-ahead distance is leveraged in a novel way to develop a two-phase guidance strategy. Initially, when the UxV is far from the reference path, an optimized $L_1$ selection strategy is developed to guide the UxV towards the vicinity of the start point of the reference path, while maintaining minimal lateral acceleration command. Once the vehicle reaches a close neighborhood of the reference path, a novel notion of corrector point is incorporated in the constant $L_1$-based guidance scheme to generate the guidance command that effectively reduces the root mean square of the cross-track error and lateral acceleration requirement thereafter. Simulation results validate satisfactory performance of this proposed corrector point and look-ahead point pair-based guidance strategy, along with the developed mid-course guidance scheme. Also, its superiority over the conventional constant $L_1$ guidance scheme is established by simulation studies over different initial condition scenarios.

We present an algorithm providing a heuristic solution to the NP-hard optimization problem known as the watchman route problem (WRP) within a 3D virtual environment testbed populated by simulated unmanned vehicles (UVs). The contribution made by our algorithm is three-fold. First, we utilize photon mapping as our means of representing the information sensed by a UV. Second, we use the photon map to generate an online solution to the closely-related NP-hard art gallery problem (AGP). Third, we use a 3D Chan-Vese segmentation algorithm initialized by our AGP-solver to produce a candidate set of path-planning waypoints. The use of photon mapping with our online AGP solver allows us to adapt UV operation to accommodate variable, less-than-ideal environmental circumstances. The use of our 3D Chan-Vese segmentation algorithm creates a set of candidate waypoints that yield greater visibility coverage when computing the WRP than would be obtainable otherwise. Our algorithm provides for quick learning among the unmanned vehicles operating within the testbed’s virtual environment by generating easily-transferrable WRP-solving waypoints.