Iman Soodmand

Papers

Total Citations

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About

Iman Soodmand is a robotics researcher specializing in motion planning and control for redundant manipulators, with a focus on enhancing the autonomy and safety of robotic systems. Their most-cited work, "An Artificial Potential Field Algorithm for Path Planning of Redundant Manipulators Based on Navigation Functions" (2022), introduces a novel approach that integrates artificial potential fields with navigation functions to enable efficient, collision-free path planning for high-degree-of-freedom robotic arms. This contribution addresses critical challenges in real-time obstacle avoidance and kinematic redundancy, offering a robust framework for applications in manufacturing, healthcare, and autonomous systems. While the paper has garnered 2 citations, it represents a foundational step in advancing path planning methodologies. Soodmand’s research bridges theoretical algorithms with practical robotic implementations, demonstrating a commitment to solving complex problems in dynamic environments. Their work is particularly valuable for students and researchers exploring intelligent control strategies, as it provides a clear, implementable solution to a classic robotics problem. With a focus on safety and efficiency, Soodmand continues to contribute to the evolving field of autonomous manipulation.