Shane Trimble

Papers

Total Citations

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About

Shane Trimble’s research lies at the intersection of collaborative robotics, nonlinear control, and STEM education. His most cited work introduces a fast embedded model predictive control (MPC) framework for context-aware robotic arms operating in unstructured environments. By addressing the highly nonconvex, nonlinear dynamics of human-robot shared spaces, this approach enables safer, faster path planning and collision avoidance—critical for next-generation collaborative robots. With 6 citations, this paper has informed advances in real-time embedded control. Trimble also contributed to slip signal analysis on the Baxter robot, a dual-arm collaborative platform, exploring how tactile feedback can improve manipulation of large or irregular objects in tandem operations. Beyond technical contributions, Trimble designed and analyzed a robotics day event aimed at pre-GCSE students, demonstrating a commitment to broadening STEM participation. This outreach work, though with 2 citations, reflects his belief that engineering innovation must be paired with educational impact. Together, his publications showcase a researcher equally invested in pushing the boundaries of robotic autonomy and inspiring the next generation of engineers.