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About

Zhiqiang Wang is a robotics researcher whose work centers on mobile robot path planning, with a particular focus on applying chaotic dynamical systems to coverage and navigation challenges. His most influential contributions lie in the development of chaos-based coverage path planning (CCPP) algorithms, where he has creatively leveraged mathematical systems — including the Lorenz attractor, Chebyshev maps, and the Standard map — to guide robots through complex environments requiring complete area coverage. His 2016 paper introducing a bounded Lorenz chaotic strategy (35 citations) and his 2017 work on the Chebyshev map-based planner (34 citations) represent landmark contributions to this niche, demonstrating that chaotic system properties such as ergodicity and sensitivity to initial conditions can be harnessed for efficient, unpredictable, yet bounded robot trajectories suited to special missions. Wang has also contributed to classical sampling-based planning, proposing a greedy RRT algorithm featuring variable sampling domains and map compression to improve exploration efficiency. Collectively, his research bridges nonlinear dynamics and autonomous robotics, offering innovative algorithmic tools for researchers developing robot systems that must navigate and fully cover structured or obstacle-laden environments.