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About

Shakeeb Ahmad is a robotics researcher specializing in autonomous navigation, motion planning, and perception for robots operating in complex, unstructured environments. His work addresses some of the most challenging problems in mobile robotics, particularly enabling reliable robot autonomy in GPS-denied and highly cluttered settings such as underground tunnels, caves, and disaster scenarios. Ahmad's most significant contributions center on robust planning architectures and probabilistic perception frameworks. His two-layer reactive-and-exploratory planning system demonstrates how combining environmental maps with real-time depth sensing enables robots to navigate safely while maximizing information gain. He has also pioneered probabilistic approaches to obstacle avoidance, formulating the problem as a Partially Observable Markov Decision Process (POMDP) to handle the inevitable noise and incompleteness of real-world sensor data. A highlight of his career is his involvement in DARPA's Subterranean Challenge, a prestigious competition that accelerated breakthrough developments in autonomous exploration. His research on flexible supervised autonomy and efficient sampling-based subterranean planners directly contributed to advances demonstrated in this high-profile program. With over 50 cumulative citations across his key publications, Ahmad's work is gaining meaningful traction in the robotics community, making him a promising voice in the field of resilient autonomous systems.