Papers
1
Total Citations
26
H-Index
1
About
Arjun Sridhar’s research lies at the intersection of robotics, manufacturing, and intelligent motion planning, with a focus on enhancing the precision and autonomy of industrial robotic systems. His most cited work, “Automatic Motion Generation for Robotic Milling Optimizing Stiffness with Sample-Based Planning” (2017, 26 citations), tackles a critical challenge in robotic machining: the inherent lack of stiffness that compromises accuracy. By integrating sample-based planning with stiffness optimization, Sridhar developed a method to automatically generate robot trajectories that maximize structural rigidity, directly improving machining quality without requiring expensive hardware modifications. This contribution is particularly impactful for high-precision applications in aerospace and automotive manufacturing, where even minor deviations can lead to costly defects. Sridhar’s approach bridges the gap between theoretical robotics and practical industrial needs, offering a computationally efficient solution that adapts to varying workpiece geometries. His work has been recognized for advancing the state of the art in robot-aware manufacturing, and he continues to explore how intelligent planning can make robotic systems more reliable, intuitive, and accessible for complex production tasks.
Research Focus
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Top Papers
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