R. Arteaga

About

R. Arteaga is a robotics researcher specializing in motion planning and kinodynamic control, with a focus on sampling-based algorithms for autonomous systems operating under complex constraints. Their most cited work, "On the Efficiency of the SST Planner to Find Time Optimal Trajectories Among Obstacles With a DDR Under Second Order Dynamics" (2021, 4 citations), addresses a fundamental challenge in robotics: computing time-optimal trajectories for differentially-driven robots (DDRs) navigating obstacle-filled environments while respecting second-order dynamics. Arteaga’s key contribution lies in demonstrating how the use of extremal controls as inputs can significantly enhance the efficiency of the Stable Sparse Tree (SST) planner, enabling it to solve kinodynamic motion planning problems—where both position and velocity constraints matter—more effectively than previous approaches. This work bridges the gap between theoretical optimal control and practical sampling-based planning, offering a scalable solution for real-world robotic applications. Though early in their career, Arteaga’s research has already laid groundwork for improving autonomous navigation in cluttered, dynamic settings, making it a valuable reference for students and researchers exploring motion planning under nonholonomic and second-order constraints.

Research Focus

Key Achievements

Top Papers

1. 1
   On the Efficiency of the SST Planner to Find Time Optimal Trajectories Among Obstacles With a DDR Under Second Order Dynamics

   4 citations · 2021

Key Collaborators

Contact & Links