Max Robotics — Global Service Robot Directory + US Certification

About

Bi Zeng is a robotics researcher whose work spans multi-robot systems, motion planning, simultaneous localization and mapping (SLAM), and visual tracking. Over nearly two decades of sustained contribution, Zeng has built a research portfolio that bridges foundational algorithmic theory with practical robotic applications. Zeng's most influential contribution is the TMSTC* algorithm, a turn-minimizing coverage path planning method for multi-robot systems that has rapidly accumulated 52 citations since 2023, reflecting its strong uptake in the robotics community. This work addresses efficiency in large-scale coverage tasks — a critical challenge in applications such as search-and-rescue and environmental monitoring. Complementing this, Zeng has advanced multi-robot motion planning by integrating flocking control with reinforcement learning (27 citations), enabling more adaptive behavior in complex, unknown environments. Beyond coordination, Zeng has contributed to lightweight visual tracking with LiteTrack (25 citations), addressing real-time constraints on edge devices, and to robust indoor localization through a novel 2D LiDAR-inertial-wheel SLAM framework (14 citations). Earlier work explored fuzzy neural networks and artificial potential fields for path planning, demonstrating a long-standing commitment to intelligent, adaptive robot navigation. Collectively, Zeng's research reflects a coherent vision: making multi-robot systems smarter, faster, and more deployable in real-world settings.