Work

Uncertainty-Aware Multi-Modal Learning for Autonomous Pallet Handling in Dynamic Warehouse Scenarios

Year: 2025

Type: article

Abstract: Autonomous pallet transport in dynamic warehouses, where human workers and automated guided vehicles (AGVs) coexist, remains highly challenging due to sensory heterogeneity, environmental uncertainty, and strict real-time requirements. To address these issues, we propose an adaptive learning framework that integrates multi-modal sensory inputs, an Interactive Multiple Model (IMM) Kalman filter for trajectory forecasting, and Proximal Policy Optimization (PPO) for policy learning. A hybrid fusion strategy—combining early latent projection and shrinkage-enhanced cross-attention—ensures temporal-spatial alignment and suppresses noisy modalities, while a multi-objective reward balances safety, efficiency, and task success. Experiments in a high-fidelity warehouse simulator show that our method achieves a 78.5 % success rate, reducing collisions by 29 % and improving trajectory stability by 38% compared to PPO, MPC, SAC, and Transformer-based baselines. Real-time evaluation further confirms an average cycle latency of 33.3 ms, meeting industrial deployment standards. These results demonstrate that the proposed IMM-PPO framework effectively unifies predictive filtering, uncertainty-aware fusion, and reinforcement learning, enabling robust and intelligent automation for real-world logistics scenarios.

Source: IEEE Access

DOI: 10.1109/access.2025.3613729

Authors: Xiaoming Zhao, Ye Yang, Yunhong Niu, Xinlei Li

Institution: Yunnan College Of Business Management

Language: English

Topic: Reinforcement Learning in Robotics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,075