  In obstacle-dense scenarios, providing safe guidance for mobile robots is
critical to improve the safe maneuvering capability. However, the guidance
provided by standard guiding vector fields (GVFs) may limit the motion
capability due to the improper curvature of the integral curve when traversing
obstacles. On the other hand, robotic system dynamics are often time-varying,
uncertain, and even unknown during the motion planning process. Therefore, many
existing kinodynamic motion planning methods could not achieve satisfactory
reliability in guaranteeing safety. To address these challenges, we propose a
two-level Vector Field-guided Learning Predictive Control (VF-LPC) approach
that improves safe maneuverability. The first level, the guiding level,
generates safe desired trajectories using the designed kinodynamic GVF,
enabling safe motion in obstacle-dense environments. The second level, the
Integrated Motion Planning and Control (IMPC) level, first uses a deep Koopman
operator to learn a nominal dynamics model offline and then updates the model
uncertainties online using sparse Gaussian processes (GPs). The learned
dynamics and a game-based safe barrier function are then incorporated into the
LPC framework to generate near-optimal planning solutions. Extensive
simulations and real-world experiments were conducted on quadrotor unmanned
aerial vehicles and unmanned ground vehicles, demonstrating that VF-LPC enables
robots to maneuver safely.
