Mingjie Cai

Nanjing University of Science and Technology, Qingdao University

Papers

Total Citations

H-Index

About

Mingjie Cai is a leading researcher in multi-agent systems and mobile robotics, with a focus on adaptive control and path planning in complex environments. Their most cited work, "Adaptive finite‐time consensus protocols for multi‐agent systems by using neural networks" (2016, 56 citations), addresses the critical challenge of achieving finite-time consensus in second-order multi-agent systems with unknown nonlinear dynamics—a problem that is notoriously difficult due to the complexity of real-world interactions. By integrating neural networks, Cai’s protocol enables robust, decentralized coordination, offering a scalable solution for applications in autonomous swarms and distributed robotics. In parallel, Cai’s research on "Car-Like Mobile Robot Path Planning in Rough Terrain With Danger Sources" (2019, 4 citations) tackles practical navigation in hazardous environments, such as battlefields or disaster zones, where traditional flat-ground assumptions fail. This work introduces algorithms that account for rough terrain and dynamic threats, enhancing robot safety and mission success. With a growing citation impact, Cai’s contributions bridge theoretical control theory and real-world robotics, making their work essential for researchers developing resilient autonomous systems.