MP-MPPI: A Motion Primitive Guided Sampling-Based Optimizer for Model Predictive Control

2026-07-07

AI Summary

This work addresses the limited exploration capability and difficulty in converging to global optima inherent in sampling-based controllers for path planning. To overcome these challenges, the authors propose integrating motion primitives into the Model Predictive Path Integral (MPPI) framework. By fusing motion primitive-guided structured sampling with perturbed control sequences within the real-time optimization loop, the method substantially enhances exploration efficiency and global optimality in the control space while preserving MPPI’s intrinsic fast response characteristics. Evaluations on quadrotor obstacle navigation tasks demonstrate that the proposed algorithm significantly improves both exploratory behavior and real-time performance, thereby validating its superiority over conventional approaches.

Abstract

This paper proposes a novel method that extends the Model Predictive Path Integral (MPPI) method with motion primitives for additional structured sampling, which enhances the convergence towards a globally optimal solution. By evaluating motion primitives and perturbed control sequences in a real-time sampling-based optimization loop, this work addresses the limitations of the path planning capabilities of sampling-based controllers. The algorithm is implemented on a quadcopter simulator and tested on an obstacle field navigation task. It is demonstrated that the proposed approach enhances exploration of the control space while maintaining the fast, reactive behavior required for real-time control.

Problem

• motion primitives
• sampling-based optimization
• model predictive control
• path planning
• control space exploration

Innovation

• motion primitives
• Model Predictive Path Integral
• sampling-based optimization
• real-time control
• quadcopter navigation