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About

Khaled Elleithy is a prominent researcher specializing in autonomous mobile robotics, sensor fusion, and intelligent navigation systems. His work has made significant contributions to the field of mobile robot navigation, particularly in developing sophisticated algorithms for collision avoidance and trajectory planning in both static and dynamic environments. Elleithy's most impactful contributions center on multi-sensor integration for autonomous robotics. His 2016 paper on trajectory planning and collision avoidance has garnered 67 citations, while his 2015 work on sensor fusion-based collision-free navigation has accumulated 59 citations, together establishing him as a key voice in real-time obstacle avoidance research. By combining sensors such as GPS, cameras, infrared, and ultrasonic devices, his research demonstrates how data fusion can dramatically improve a robot's environmental awareness and decision-making capabilities. Beyond hardware integration, Elleithy has advanced the application of fuzzy logic systems in mobile robotics, enabling more adaptive responses to complex environments. His 2017 work further extended these contributions by addressing energy efficiency in multi-sensor path planning — a critical consideration for practical deployment. His edited volume on technological developments in networking, education, and automation reflects the breadth of his academic interests, making him a versatile and impactful figure across multiple engineering disciplines.