Akash Mecwan

Papers

Total Citations

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About

Akash Mecwan is a robotics researcher whose work bridges autonomous navigation, embedded systems, and real-time computer vision. His research focuses on developing efficient, low-latency solutions for mobile robotics, particularly in path planning and environmental perception. Mecwan’s most cited work, “SD-YOLOv5: Implementation of Real-Time Staircase Detection on Jetson Nano Board” (2025), demonstrates his expertise in deploying deep learning models on resource-constrained hardware, achieving practical obstacle detection for assistive and autonomous robots. His earlier contribution, “Path Tracing in Holonomic Drive System with Reduced Overshoot using Rotary Encoders” (2020), addresses a fundamental challenge in omnidirectional robot control—accurately following complex trajectories (curved or linear) by sampling path coordinates into local memory and retrieving them dynamically during runtime, minimizing overshoot. This work is critical for precise motion in tight spaces. With both papers accumulating 2 citations each, Mecwan’s impact is emerging in applied robotics, where his integration of YOLOv5 with edge computing and holonomic drive optimization offers scalable solutions for real-world navigation. His achievements highlight a commitment to making robotics more responsive and computationally efficient, appealing to students and researchers interested in embedded AI and motion control.