The classical Model Predictive Path Integral (MPPI) control framework lacks
reliable safety guarantees since it relies on a risk-neutral trajectory
evaluation technique, which can present challenges for safety-critical
applications such as autonomous driving. Additionally, if the majority of MPPI
sampled trajectories concentrate in high-cost regions, it may generate an
infeasible control sequence. To address this challenge, we propose the U-MPPI
control strategy, a novel methodology that can effectively manage system
uncertainties while integrating a more efficient trajectory sampling strategy.
The core concept is to leverage the Unscented Transform (UT) to propagate not
only the mean but also the covariance of the system dynamics, going beyond the
traditional MPPI method. As a result, it introduces a novel and more efficient
trajectory sampling strategy, significantly enhancing state-space exploration
and ultimately reducing the risk of being trapped in local minima. Furthermore,
by leveraging the uncertainty information provided by UT, we incorporate a
risk-sensitive cost function that explicitly accounts for risk or uncertainty
throughout the trajectory evaluation process, resulting in a more resilient
control system capable of handling uncertain conditions. By conducting
extensive simulations of 2D aggressive autonomous navigation in both known and
unknown cluttered environments, we verify the efficiency and robustness of our
proposed U-MPPI control strategy compared to the baseline MPPI. We further
validate the practicality of U-MPPI through real-world demonstrations in
unknown cluttered environments, showcasing its superior ability to incorporate
both the UT and local costmap into the optimization problem without introducing
additional complexity.Comment: This paper has 15 pages, 10 figures, 4 table