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About

Heikel Yervilla-Herrera is a robotics researcher whose work lies at the intersection of motion planning, nonholonomic systems, and autonomous object reconstruction. His most significant contribution, published in 2021, explores the properties of local planners within the RRT* framework for dynamical systems, demonstrating how these planners can be reused for compound cost functionals to achieve asymptotic global optimality. This work, which has garnered 15 citations, provides formal results on length-optimal trajectories under nonholonomic metrics, advancing the theoretical foundations of sampling-based planning. Yervilla-Herrera also tackles the practical challenge of 3-D object reconstruction, developing optimal motion planning strategies and Bayesian probabilistic stopping tests for mobile manipulator robots. His 2018 paper on this topic, with 7 citations, and a subsequent 2022 extension, which incorporates asymptotic shortest-time trajectories, showcase his ability to bridge theory and application. By unifying optimal control with probabilistic decision-making, Yervilla-Herrera’s research enables more efficient and reliable autonomous systems, making him a notable contributor to the fields of robotics and automation.