Ansi Zhang

Papers

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About

Ansi Zhang is a researcher advancing the frontiers of intelligent robotics, with a primary focus on path planning and trajectory optimization for autonomous systems. His work addresses critical challenges in mobile robot navigation, particularly the trade-off between real-time obstacle avoidance and global path efficiency. In his highly cited 2021 study on intelligent optimization algorithms for mobile robot path planning (51 citations), Zhang introduced novel approaches that significantly improve the accuracy of local obstacle avoidance predictions while reducing overall path length—a persistent challenge in the field. His contributions extend to robotic manipulators, where his 2021 work on RBF neural network-based trajectory planning (34 citations) tackles the nonlinearity and uncertainty issues that have long plagued controller design. By leveraging neural network architectures, Zhang has developed more robust controllers that enhance trajectory accuracy for industrial manipulators. His research bridges the gap between theoretical optimization algorithms and practical robotic applications, offering solutions that improve both safety and efficiency in autonomous navigation. Zhang’s work is particularly relevant for researchers and engineers developing next-generation mobile robots and industrial automation systems.