You Li

Runhao Liu, Zhengyang Cheng, Fei Ding et al.

High-mix manufacturing systems require production plans that are both profitable and refinable into executable machine-level schedules under heterogeneous resources, mold-dependent compatibility, setup losses,delivery windows, and accessory synchronization. We study this problem as a production-envelope refinement task. A rolling-horizon mixed-integer linear programming (MILP) planner generates a valid production envelope that fixes daily production, fulfillment, mold states, inventory flows, outsourcing, and unmet-demand variables. A structure-aware constructive scheduler then refines this envelope into concrete order-machine allocations while preserving capacity feasibility, product-mold-machine compatibility, and delivery-window compliance. The scheduler enforces a one-mold-per-machine-per-day stability rule to avoid intra-day mold fragmentation. We establish residual invariants and prove a soundness theorem: whenever refinement terminates with zero residual fulfillment, the returned allocation is executable with respect to the valid envelope. The framework is implemented as an Advanced Planning and Scheduling (APS) prototype and evaluated on a real industrial case from a Jiangsu smartphone-case manufacturer in China with 37 product types, 150 orders, and over 8.3 million requested units. The proposed stable refinement achieves 100% on-time delivery, eliminates outsourcing, and bounds changeover-driven capacity loss to 1.9-4.6%. Across nine demand and changeover perturbation scenarios, it maintains robust delivery performance, showing that sound envelope refinement is a practical mechanism for reliable manufacturing scheduling.

Ning Cheng, You Li, Jing Gao et al.

Tactility provides crucial support and enhancement for the perception and interaction capabilities of both humans and robots. Nevertheless, the multimodal research related to touch primarily focuses on visual and tactile modalities, with limited exploration in the domain of language. Beyond vocabulary, sentence-level descriptions contain richer semantics. Based on this, we construct a touch-language-vision dataset named TLV (Touch-Language-Vision) by human-machine cascade collaboration, featuring sentence-level descriptions for multimode alignment. The new dataset is used to fine-tune our proposed lightweight training framework, STLV-Align (Synergistic Touch-Language-Vision Alignment), achieving effective semantic alignment with minimal parameter adjustments (1%). Project Page: https://xiaoen0.github.io/touch.page/.

Tao Yang, You Li, Cheng Zhao et al.

In the past ten years, the use of 3D Time-of-Flight (ToF) LiDARs in mobile robotics has grown rapidly. Based on our accumulation of relevant research, this article systematically reviews and analyzes the use 3D ToF LiDARs in research and industrial applications. The former includes object detection, robot localization, long-term autonomy, LiDAR data processing under adverse weather conditions, and sensor fusion. The latter encompasses service robots, assisted and autonomous driving, and recent applications performed in response to public health crises. We hope that our efforts can effectively provide readers with relevant references and promote the deployment of existing mature technologies in real-world systems.