Print Email Facebook Twitter Biased-MPPI Title Biased-MPPI: Informing Sampling-Based Model Predictive Control by Fusing Ancillary Controllers Author Trevisan, E. (TU Delft Learning & Autonomous Control) Alonso-Mora, J. (TU Delft Learning & Autonomous Control) Date 2024 Abstract Motion planning for autonomous robots in dynamic environments poses numerous challenges due to uncertainties in the robot's dynamics and interaction with other agents. Sampling-based MPC approaches, such as Model Predictive Path Integral (MPPI) control, have shown promise in addressing these complex motion planning problems. However, the performance of MPPI relies heavily on the choice of sampling distribution. Existing literature often uses the previously computed input sequence as the mean of a Gaussian distribution for sampling, leading to potential failures and local minima. We propose a novel derivation of MPPI that allows for arbitrary sampling distributions to enhance efficiency, robustness, and convergence while alleviating the problem of local minima. We present an efficient importance sampling scheme that combines classical and learning-based ancillary controllers simultaneously, resulting in more informative sampling and control fusion. Several simulated and real-world demonstrate the validity of our approach. Subject Collision AvoidanceCostsMathematical modelsMonte Carlo methodsMotion and Path PlanningMPPIOptimal controlOptimization and Optimal ControlPlanningSampling-based MPCTrajectoryVehicle dynamics To reference this document use: http://resolver.tudelft.nl/uuid:fb68b8d6-1ed2-4a27-ac6e-df3a478ef4d7 DOI https://doi.org/10.1109/LRA.2024.3397083 Embargo date 2024-11-06 ISSN 2377-3766 Source IEEE Robotics and Automation Letters, 9 (6), 5871-5878 Bibliographical note Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Part of collection Institutional Repository Document type journal article Rights © 2024 E. Trevisan, J. Alonso-Mora Files file embargo until 2024-11-06