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About

Qichao Tang is a robotics and automation researcher whose work centers on intelligent path planning, multi-robot coordination, and optimization algorithms for industrial applications. His most significant contributions lie in developing sophisticated computational methods to solve complex planning challenges in robotic welding and autonomous inspection systems. Tang's most cited work, "A Dual-Robot Cooperative Arc Welding Path Planning Algorithm based on Multi-Objective Cross-Entropy Optimization" (2024, 23 citations), exemplifies his signature approach: combining metaheuristic optimization techniques with practical industrial robotics problems. Across a series of influential papers, he has systematically explored diverse algorithmic strategies — including ant colony optimization, genetic algorithms (notably NSGA-II variants), cross-entropy methods, and rapidly-exploring random trees — applying them to the demanding challenge of coordinating dual-robot welding systems for large, complex components. His 2020 work on heuristic path planning for substation inspection robots (12 citations) demonstrates his broader interest in real-world autonomous systems beyond manufacturing. Throughout his research, Tang consistently prioritizes solutions that are low-cost, computationally efficient, and practically deployable. With a growing publication record and cumulative citations approaching 55, he is establishing himself as a productive contributor to intelligent robotics and combinatorial optimization in automation engineering.