# Autonomous Navigation of AGVs in Unknown Cluttered Environments: log-MPPI Control Strategy

arXiv: https://arxiv.org/abs/2203.16599 · HTML: https://mlsanity.com/paper/2203.16599/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2022.

## Built on by (most cited first, 9 of 9)

- [Adaptive Diffusion Terrain Generator for Autonomous Uneven Terrain Navigation](https://mlsanity.com/paper/2410.10766/) (2024)
- [Learning Dynamic Cognitive Map with Autonomous Navigation](https://mlsanity.com/paper/2411.08447/) (2024)
- [Action Flow Matching for Continual Robot Learning](https://mlsanity.com/paper/2504.18471/) (2025)
- [DiSProD: Differentiable Symbolic Propagation of Distributions for Planning](https://mlsanity.com/paper/2302.01491/) (2023)
- [Dyna-LfLH: Learning Agile Navigation in Dynamic Environments from Learned Hallucination](https://mlsanity.com/paper/2403.17231/) (2024)
- [BiC-MPPI: Goal-Pursuing, Sampling-Based Bidirectional Rollout Clustering Path Integral for Trajectory Optimization](https://mlsanity.com/paper/2410.06493/) (2024)
- [GP-guided MPPI for Efficient Navigation in Complex Unknown Cluttered Environments](https://mlsanity.com/paper/2307.04019/) (2023)
- [Dynamic Neural Potential Field: Online Trajectory Optimization in the Presence of Moving Obstacles](https://mlsanity.com/paper/2410.06819/) (2024)
- [ADEPT: Adaptive Diffusion Environment for Policy Transfer Sim-to-Real](https://mlsanity.com/paper/2506.01759/) (2025)

## Abstract (arXiv)

Sampling-based model predictive control (MPC) optimization methods, such as Model Predictive Path Integral (MPPI), have recently shown promising results in various robotic tasks. However, it might produce an infeasible trajectory when the distributions of all sampled trajectories are concentrated within high-cost even infeasible regions. In this study, we propose a new method called log-MPPI equipped with a more effective trajectory sampling distribution policy which significantly improves the trajectory feasibility in terms of satisfying system constraints. The key point is to draw the trajectory samples from the normal log-normal (NLN) mixture distribution, rather than from Gaussian distribution. Furthermore, this work presents a method for collision-free navigation in unknown cluttered environments by incorporating the 2D occupancy grid map into the optimization problem of the sampling-based MPC algorithm. We first validate the efficiency and robustness of our proposed control strategy through extensive simulations of 2D autonomous navigation in different types of cluttered environments as well as the cartpole swing-up task. We further demonstrate, through real-world experiments, the applicability of log-MPPI for performing a 2D grid-based collision-free navigation in an unknown cluttered environment, showing its superiority to be utilized with the local costmap without adding additional complexity to the optimization problem. A video demonstrating the real-world and simulation results is available at https://youtu.be/_uGWQEFJSN0.