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About

Pengda Mao is an emerging researcher specializing in swarm robotics, trajectory planning, and autonomous navigation in complex environments. His work centers on the development of virtual tube frameworks — innovative spatial constructs that enable large numbers of robots to navigate safely and efficiently through obstacle-dense settings. In his highly cited 2023 paper on optimal virtual tube planning and control (34 citations), Mao introduced a novel method that significantly advances real-time local trajectory optimization for robot swarms in cluttered environments. Building on this foundation, his 2022 work on the Regular Virtual Tube Model (24 citations) established principled conditions for generating safe, feasible, and smooth navigational spaces, particularly for drone swarms operating under flocking models. His 2025 contribution, Tube RRT* (15 citations), addresses the critical gap in homotopic path planning for large-scale obstacle environments, offering a computationally efficient solution with broad practical applications. More recently, Mao has explored the intersection of iterative learning and mean-field theory to optimize swarm navigation efficiency. With nearly 80 cumulative citations across just four papers, his contributions are rapidly shaping the theoretical and applied landscape of intelligent multi-robot systems.