Enggar Alfianto

Papers

Total Citations

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About

Enggar Alfianto is a researcher specializing in robotics, control systems, and computer vision, with a focus on autonomous navigation and manipulator design. His most cited work, “Simulation design of trajectory planning robot manipulator” (2019, 19 citations), introduces a mathematical modeling approach for robot motion control, enabling visual simulation to optimize input, gain, and error parameters—a foundational contribution for precision in robotic arm trajectory planning. Alfianto also advanced obstacle detection for autonomous vehicles in “PEMBUATAN HAAR-CASCADE DAN LOCAL BINARY PATTERN SEBAGAI SISTEM PENDETEKSI HALANGAN PADA AUTOMATIC GUIDED VEHICLE” (2018, 3 citations), where he applied Haar-cascade and Local Binary Pattern algorithms to develop a real-time obstacle detection system for Automatic Guided Vehicles (AGVs) in non-sterile campus environments. This work addresses practical challenges in deploying AGVs in dynamic, real-world settings. With a growing citation impact, Alfianto’s research bridges simulation and application, offering scalable solutions for intelligent robotics. His achievements highlight a commitment to enhancing autonomous systems’ reliability and adaptability, making him a notable contributor to the fields of robotics and automation engineering.