Papers
8
Total Citations
250
H-Index
5
About
Lisang Liu is a leading researcher in mobile robotics, specializing in intelligent path-planning algorithms that enable autonomous navigation in complex, dynamic environments. Their work has garnered over 250 citations, reflecting significant impact in the field. Liu’s core contributions center on fusing classical and bio-inspired algorithms to overcome the limitations of individual methods. Notably, their 2022 paper on integrating an optimized A-star algorithm with the artificial potential field method (108 citations) achieved a breakthrough in path smoothness and computational speed for home cleaning robots. They further advanced the state of the art by combining Jump-A* with the Dynamic Window Approach for global dynamic planning (80 citations) and by developing an improved Sparrow Search Algorithm to reduce collision risks and path lengths in mobile robots (41 citations). Liu has also explored reinforcement learning for adaptive path planning and proposed robust force/position regulators for robot manipulators under uncertainty. Their recent work on a restart-strategy particle swarm algorithm and an improved Grey Wolf Optimizer continues to push boundaries in dynamic obstacle avoidance. Through these innovations, Liu has become a key figure in making robots safer, faster, and more reliable in real-world applications.
Research Focus
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