Papers
1
Total Citations
2
H-Index
1
About
Hafiz Iman is a robotics researcher whose work focuses on motion planning and obstacle avoidance for robotic manipulators. His primary research area lies in developing reactive planning strategies that enable robotic arms to navigate unpredictable environments safely. In his most-cited work, "Repurposing A Sampling-Based Planner for A Six-Degree-Of-Freedom Manipulator to Avoid Unpredictable Obstacles" (2023), Iman addresses a critical challenge in robotics: how to adapt sampling-based planners—traditionally used for static environments—for real-time collision avoidance with moving obstacles. By benchmarking various planners in obstacle-ridden settings, he demonstrated that a rapidly-exploring random tree (RRT) planner can be effectively repurposed as a reactive scheme, allowing a six-degree-of-freedom manipulator to dynamically avoid unpredictable obstacles. This contribution is particularly valuable for applications in manufacturing, service robotics, and human-robot collaboration, where safety and adaptability are paramount. With 2 citations to date, his work is gaining recognition among researchers exploring real-time motion planning. Iman’s research bridges the gap between theoretical planning algorithms and practical robotic systems, offering a scalable solution for safer human-robot interaction.
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Top Papers
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