Boliang Cai
Papers
7
Total Citations
226
H-Index
5
About
Boliang Cai is a robotics and autonomous systems researcher whose work spans multi-robot coordination, deep reinforcement learning, and human-robot collaboration. His most influential contribution, "Particle Swarm Optimization for Cooperative Multi-Robot Task Allocation: A Multi-Objective Approach" (2020, 159 citations), introduced a novel MOPSO framework that simultaneously minimizes team operational cost while balancing individual robot workloads — a significant advance in multi-robot systems design. Building on this foundation, Cai has made substantial contributions to mapless navigation, developing deep reinforcement learning architectures for autonomous robots operating in unstructured, partially observable environments, including AGV systems capable of avoiding dynamic obstacles without pre-built maps. More recently, Cai has extended his research into human-robot collaborative assembly, pioneering a human digital twin approach that enables real-time physical fatigue assessment to dynamically guide task allocation between humans and robots (2024, 23 citations). His 2024 work on decentralized multi-robot path planning using multicritic TD3 further addresses real-world communication and computational constraints. Collectively, Cai's research bridges intelligent optimization, autonomous navigation, and ergonomics-aware collaboration, positioning him as an emerging voice in adaptive, human-centric robotic systems for next-generation manufacturing environments.
Research Focus
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Top Papers
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