Papers
2
Total Citations
23
H-Index
1
About
Dr. Yogesh Chawla is at the forefront of intelligent robotics and precision agriculture, pioneering deep-learning solutions for autonomous systems. His primary research focuses on computer vision and multi-robot coordination, with groundbreaking work in agricultural robotics and UAV localization. His most influential contribution is a two-stage deep-learning model for detecting and classifying Kashmiri orchard apples under occlusion, a critical advancement for robotic harvesting that has already garnered 22 citations. This work directly addresses the challenge of accurately identifying fruit in complex, natural environments, enabling more efficient and automated harvesting systems. More recently, Dr. Chawla has advanced cooperative localization for multi-UAV systems, developing deep learning-based detection methods that allow drones to precisely determine their positions relative to one another without relying on GPS. This innovation is vital for coordinated missions in agriculture, disaster response, and environmental monitoring, where robust localization is essential. With a growing citation record and a clear trajectory toward solving real-world deployment challenges, Dr. Chawla’s research is shaping the future of autonomous, multi-agent robotic systems in unstructured environments.
Research Focus
Key Achievements
Top Papers
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- 2