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About

Dijian Chen is a robotics researcher whose work spans bio-inspired locomotion, optimal control, and soft actuation. His core contributions lie in advancing the mobility and efficiency of legged robots through kinematic modeling and biologically motivated design. Chen’s most cited work, “Hexapod robot kinematics modeling and tripod gait design based on the foot end trajectory” (2017, 20 citations), draws inspiration from ant locomotion to develop a hexapod robot with enhanced crawling capabilities, establishing a foundation for gait analysis using Denavit–Hartenberg parameters. He further explores spinal mechanics in “Parallel Spine Design and CPG Motion Test of Quadruped Robot” (2019), integrating Central Pattern Generators to improve stability in quadrupedal locomotion. In the domain of energy efficiency, Chen’s “Energy-optimal trajectory generation for robot manipulators via Hamilton-Jacobi theory” (2016) provides a theoretical framework for minimizing energy consumption in robotic arms. His work on pneumatic artificial muscles (2020) introduces novel impact detection methods using differential pressure signals, addressing safety in soft robotics. With a growing citation footprint, Chen’s research is shaping the future of adaptive, energy-aware, and resilient robotic systems.