Clustering-Embedded MPPI: Avoiding Averaging-Induced Failure and Enabling Efficient Cluster Selection for Dynamic Obstacles
Abstract
With the widespread availability of parallel computing hardware, sampling-based motion planning methods such as Model Predictive Path Integral (MPPI) control have become increasingly powerful for complex nonlinear systems in non-smooth task spaces. However, the sampling and forward-simulation pipeline in MPPI suffers from averaging-induced failure in cluttered environments, where the importance-weighted update averages incompatible rollouts and leads to hesitation or even collision when an obstacle lies directly ahead. This paper proposes Clustering-Embedded MPPI (CE-MPPI), a framework that integrates a high-fidelity pruning and clustering stage, a geometric direction feature, and intelligent cluster-selection logic. Experiments in JAX-accelerated simulations and on a UR5e manipulator show faster, shorter, and safer motion around dynamic obstacles.
Zidong Liu
Ph.D. student in Mechanical Engineering
