Jun Jet Tai

Coventry University, Taylor's University

Papers

Total Citations

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About

Jun Jet Tai is a researcher specializing in autonomous systems, with a particular focus on obstacle avoidance and navigation (OAN) algorithms for unmanned aerial vehicles (UAVs). His work bridges the critical gap between offline and online navigation methods, addressing the trade-off between computational efficiency and adaptability in partially observable environments. Tai’s most cited paper, "COAA* — An Optimized Obstacle Avoidance and Navigational Algorithm for UAVs Operating in Partially Observable 2D Environments" (2021), has garnered 7 citations and introduces a novel algorithm that balances speed and configurability without requiring a global map. His earlier work, "Optimized autonomous UAV design with obstacle avoidance capability" (2020, 6 citations), further explores the design of UAVs that can navigate unknown environments with reduced computational overhead. Together, these contributions have laid foundational groundwork for more practical, real-time UAV navigation in dynamic settings. Tai’s research is particularly impactful for students and engineers working on autonomous drones, robotics, and AI-driven path planning, offering scalable solutions that move beyond traditional offline or computationally heavy online methods.