Papers
3
Total Citations
134
H-Index
3
About
Joe Sfeir is a robotics researcher whose primary focus lies in autonomous mobile robot navigation, particularly in unknown and dynamic environments. His most significant contribution is the development of an improved Artificial Potential Field (APF) approach for real-time path planning, detailed in his highly cited 2011 paper (125 citations). This work addresses a fundamental challenge in robotics: enabling a robot to safely reach a target by treating it as an attractive force while obstacles generate repulsive forces, all without requiring a pre-mapped environment. Sfeir’s innovation lies in enhancing the traditional APF method to overcome local minima issues and improve real-time performance, making it practical for real-world applications. His earlier research also explored neural-network-based path generation (2004), demonstrating a unified approach for trajectory control and obstacle avoidance using memory neuron networks. While his 2009 French-language paper on the same theme has fewer citations, it underscores his sustained commitment to sensor-efficient, computationally lightweight navigation solutions. Sfeir’s work is particularly impactful for students and engineers developing low-cost, reactive robotic systems, offering a robust foundation for autonomous navigation in cluttered, unpredictable settings.
Research Focus
Key Achievements
Top Papers
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- 3A neural-network-based path generation technique for mobile robots4 citations · 2004