Max Robotics — Global Service Robot Directory + US Certification

About

Yongqiang Cheng is a robotics and intelligent systems researcher whose work spans autonomous navigation, computer vision, and AI-driven robotic applications. His research consistently bridges theoretical algorithms with real-world deployment, addressing core challenges in robot path planning, object recognition, and intelligent delivery systems. Cheng's most influential contribution — his 2023 work on obstacle avoidance using an improved artificial potential field method (49 citations) — advances trajectory planning for collaborative robot arms in demanding medical and surgical contexts. Earlier foundational work explored distributed snake algorithms and wireless sensor-supported navigation, demonstrating a long-standing interest in offloading computational complexity from onboard systems to environmental intelligence networks. His 2014 multiple Bloom filter design for distributed navigation routing (22 citations) reflects innovative thinking at the intersection of robotics and resource-constrained wireless sensor systems. In computer vision, Cheng developed deep learning pipelines for indoor object recognition using convolutional neural networks and prior knowledge integration, achieving strong results for mobile robot navigation. More recently, he has contributed to AI-driven autonomous delivery systems and transformer-based social relation recognition, reflecting a broadening scope toward human-robot interaction and smart logistics. With over 170 cumulative citations, his body of work represents meaningful, sustained progress in making robots more perceptive, adaptive, and autonomous in real-world environments.