Shaobo Wu

Beijing University of Posts and Telecommunications, Beihang University

Papers

Total Citations

H-Index

About

Shaobo Wu is a researcher at the forefront of intelligent robotics and logistics automation, with a focus on multi-robot coordination and deep learning-driven visual systems. His work addresses critical challenges in dynamic, unstructured environments—from disorderly parcel sorting to obstacle-laden terrain. In his most cited paper, "Visual Sorting of Express Parcels Based on Multi-Task Deep Learning" (2020, 31 citations), Wu introduced a novel framework that enables accurate detection and sorting of randomly stacked parcels, a breakthrough for intelligent logistics systems. He further advanced multi-robot collaboration with "Adaptive virtual leader–leader–follower based formation switching for multiple autonomous tracked mobile robots in unknown obstacle environments" (2024, 14 citations), proposing a flexible formation control strategy that allows robots to adaptively navigate complex spaces. Earlier, Wu demonstrated his versatility by designing a cloud-based remote control system for mobile robots (2017, 4 citations), integrating IoT and Android interfaces for user-friendly operation. With a growing citation record, Wu’s contributions are shaping the next generation of autonomous systems, offering practical solutions for industrial automation and mobile robotics. His work is a must-read for researchers interested in the intersection of computer vision, multi-agent systems, and real-world deployment.