Single-Instance Sampling for Computationally Efficient and Accurate Real-Time Task Space MPPI Control

Dongwhan Kim

doi:10.1109/TRO.2025.3626660

Single-Instance Sampling for Computationally Efficient and Accurate Real-Time Task Space MPPI Control

2025-01-01

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DOI：10.1109/TRO.2025.3626660

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This study presents a model predictive path integral (MPPI) method capable of conducting high-frequency real-time model predictive control (MPC) for robot manipulators. Real-time MPC-based manipulation holds significant potential for controlling an end-effector precisely and reactively while satisfying various constraints in dynamic environments. However, the optimization under a complex robot model and various constraints imposes a heavy computational burden, hindering the realization of high-frequency updates. To address this challenge, we propose a single-instance sampling-based MPPI algorithm and dynamic time horizon to significantly reduce the computational burden while enhancing control performance. The performance and efficacy of the proposed method are verified through experiments conducted on a 7-degree-of-freedom robotic arm, along with comparative simulations and analysis.

Keywords:

Manipulator control

model predictive path integral (MPPI)

optimal control

real-time control

Single-Instance Sampling for Computationally Efficient and Accurate Real-Time Task Space MPPI Control

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