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About

Mansoor Khaksar is a leading researcher in robotics and autonomous navigation, specializing in motion planning for mobile robots operating in unknown environments. His work addresses one of the most challenging problems in robotics: enabling robots to navigate safely and efficiently without prior maps, relying solely on real-time sensor data. Khaksar’s major contributions center on integrating sampling-based path planning with advanced computational intelligence techniques, particularly Tabu search and fuzzy logic systems. His most-cited paper, "Sampling-Based Tabu Search Approach for Online Path Planning" (2012, 26 citations), pioneered a novel method that combines the low-memory advantages of sampling-based planning with the optimization power of Tabu search, significantly improving real-time decision-making. He further advanced this framework by incorporating adaptive neuro-fuzzy inference systems (ANFIS) and genetic algorithms, as seen in his 2017 and 2013 works, creating robust, adaptive controllers that handle dynamic obstacles and sensor uncertainty. With a cumulative citation count exceeding 60, Khaksar’s research has been instrumental in bridging the gap between theoretical path planning algorithms and practical, real-world robotic applications. His work remains essential reading for students and engineers developing autonomous systems for search-and-rescue, exploration, and industrial automation.