Receding Horizon Extended Linear Quadratic Regulator for RFS-Based Swarms With Target Planning and Automatic Cost Function Scaling

摘要

A cost function is constructed for the random finite-set-based swarm guidance problem mechanized by Gaussian mixtures. This cost function uses an automated problem-dependent scaling and introduces an activator function for quadratic convergence of far off Gaussians. The cost function also depends on a target planner to transform user-defined waypoints into "way-areas" by calculating a covariance matrix. These covariances are based upon the given distribution of the target/reference geometry. Then an extended linear-quadratic regulator (LQR) is defined for the swarm problem as an improvement to the iterative LQR (ILQR). The algorithm is tested in a software simulation, and it is found to have easier tuning than ILQR and generates smooth trajectories toward the targets.