Papers
32
Total Citations
542
H-Index
14
About
Xinde Li is a prominent robotics and intelligent systems researcher whose work spans autonomous navigation, multi-robot coordination, human-robot interaction, and information fusion. His research has made significant contributions to the field of mobile robotics, particularly through the development of biologically inspired algorithms that enable robots to operate effectively in complex, unknown environments. Li's landmark 2016 paper on neural-dynamics-driven cooperative area coverage (109 citations) demonstrated how multiple robots could collaboratively and efficiently navigate shared workspaces, substantially reducing task completion time. His earlier work on sonar-based map building using the Dezert-Smarandanche Theory (DSmT) addressed the critical challenge of uncertainty in robotic perception, while his sensor-based autonomous navigation framework provided practical solutions for real-time mapping and collision avoidance. More recently, Li has advanced human-robot collaboration through skill learning strategies built on dynamic motion primitives, and has pioneered deep reinforcement learning approaches with novel dense reward mechanisms for manipulator trajectory planning. His self-supervised grasp planning research further reflects his commitment to reducing data acquisition burdens in robotic grasping systems. With over 380 cumulative citations, Li's interdisciplinary contributions continue to shape the future of intelligent, adaptive robotic systems across both ground and aerial platforms.
Research Focus
Key Achievements
Top Papers
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- 5A Self-Supervised Learning-Based 6-DOF Grasp Planning Method for Manipulator30 citations · 2021
- 6Robot Map Building From Sonar Sensors and DSmT30 citations · 2006
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- 8Modeling and controlling of quadrotor aerial vehicle equipped with a gripper22 citations · 2019
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- 10Visual navigation method for indoor mobile robot based on extended BoW model16 citations · 2017