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About

Heshan Fernando is a leading researcher in autonomous heavy machinery, with a focus on robotic excavation and material classification. His work bridges proprioceptive sensing and machine learning to enable intelligent, adaptive control for construction and industrial equipment. Fernando’s most cited paper, “What lies beneath: Material classification for autonomous excavators using proprioceptive force sensing and machine learning” (2020, 40 citations), introduces a novel method for identifying subsurface materials through force feedback, a critical step toward fully autonomous excavation. His earlier contributions, including “Iterative Learning-Based Admittance Control for Autonomous Excavation” (2019, 33 citations) and “Towards Controlling Bucket Fill Factor in Robotic Excavation by Learning Admittance Control Setpoints” (2017, 16 citations), demonstrate a sustained effort to optimize bucket fill efficiency through iterative learning and adaptive admittance control. Most recently, Fernando has extended his expertise to the timber-harvesting industry with “Log Loading Automation for Timber-Harvesting Industry” (2024, 4 citations), addressing a long-standing automation gap in forestry logistics. With over 90 total citations and a clear trajectory from foundational sensing to applied industrial automation, Fernando’s work is shaping the future of autonomous earthmoving and material handling.