This paper presents a novel Stochastic Optimal Control (SOC) method based on Model Predictive Path Integral control (MPPI), named Stein Variational Guided MPPI (SVG-MPPI), designed to handle rapidly shifting multimodal optimal action distributions. While MPPI can find a Gaussian-approximated optimal action distribution in closed form, i.e., without iterative solution updates, it struggles with the mul-timodality of the optimal distributions. This is due to the less representative nature of the Gaussian. To overcome this limitation, our method aims to identify a target mode of the optimal distribution and guide the solution to converge to fit it. In the proposed method, the target mode is roughly estimated using a modified Stein Variational Gradient Descent (SVGD) method and embedded into the MPPI algorithm to find a closed-form "mode-seeking" solution that covers only the target mode, thus preserving the fast convergence property of MPPI. Our simulation and real-world experimental results demonstrate that SVG-MPPI outperforms both the original MPPI and other state-of-the-art sampling-based SOC algorithms in terms of path-tracking and obstacle-avoidance capabilities. https://github.com/kohonda/proj-svg_mppi

Four-wheel independent drive and steering vehicle (4WIDS Vehicle, Swerve Drive Robot) has the ability to move in any direction by its eight degrees of freedom (DoF) control inputs. Although the high maneuverability enables efficient navigation in narrow spaces, obtaining the optimal command is challenging due to the high dimension of the solution space. This paper presents a navigation architecture using the Model Predictive Path Integral (MPPI) control algorithm to avoid collisions with obstacles of any shape and reach a goal point. The key idea to make the problem easier is to explore the optimal control input in a reasonably reduced dimension that is adequate for navigation. Through evaluation in simulation, we found that the selecting sampling space of MPPI greatly affects navigation performance. In addition, our proposed controller which switches multiple sampling spaces according to the real-time situation can achieve balanced behavior between efficiency and safety.Source code is available at https://github.com/MizuhoAOKI/mppi_swerve_drive_ros.

This study compares and evaluates the effect of the choice of the vehicle's prediction model on the performance in designing a path-tracking controller for vehicles using Model Predictive Control (MPC). The Kinematic Ackermann Model (KAM), the Kinematic Bicycle Model (KBM), and the Dynamic Bicycle Model (DBM) are well known as nonlinear prediction models. The stability and tracking performance of these models are evaluated using simulations, and a newly proposed DBM improved in Low-speed range (DBM-L) is also compared. As a result of the simulation, the proposed DBM-L was able to run in the widest 0 to 120km/h speed range among the models tested, and it was able to achieve the stop-and-go behavior that was not possible with the conventional DBM. In the future, if we can solve the problem that the tracking accuracy of the DBM-L is slightly decreased in the extremely low and high speed ranges, a vehicle prediction model that can be used in all speed ranges is expected to be realized.