Papers
9
Total Citations
141
H-Index
6
About
Minghan Wei is a robotics researcher specializing in energy-efficient robot navigation, coverage path planning, and autonomous mobile systems. His work addresses a fundamental challenge in practical robotics: enabling robots to operate intelligently under real-world energy constraints rather than idealized assumptions. Wei's most influential contribution, "Coverage Path Planning Under the Energy Constraint" (2018, 67 citations), tackled the critical gap between theoretical coverage planning and battery-limited real-world deployments, offering algorithmic solutions including a log-approximation approach that remains widely referenced in the field. Building on this foundation, he pioneered collaborative air-ground robot frameworks, leveraging aerial imagery and ground measurements to construct energy-cost maps for uneven terrains — a particularly challenging problem for outdoor agricultural and field robotics applications. His later work expanded into machine learning-driven solutions, including semi-supervised deep learning for energy mapping and occupancy map inpainting to overcome sensor occlusion limitations in indoor navigation. More recently, Wei has explored reinforcement learning approaches, applying PPO-based methods for autonomous goal-seeking and collision avoidance in dynamic environments. With over 140 cumulative citations, Wei's research consistently bridges theoretical algorithmic rigor with practical deployment challenges, making him a valuable voice in the growing field of energy-aware autonomous robotics.
Research Focus
Key Achievements
Top Papers
- 1Coverage Path Planning Under the Energy Constraint67 citations · 2018
- 2Predicting Energy Consumption of Ground Robots on Uneven Terrains21 citations · 2021
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- 4A Log-Approximation for Coverage Path Planning with the Energy Constraint11 citations · 2018
- 5Occupancy Map Inpainting for Online Robot Navigation10 citations · 2021
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