Papers
12
Total Citations
72
H-Index
6
About
Sekou Diane is a robotics researcher whose work sits at the intersection of autonomous systems, multi-robot coordination, and intelligent control. His research focuses primarily on developing adaptive algorithms and planning frameworks that enable groups of mobile robots to operate effectively in complex, partially known environments. Diane's most influential contribution — an adaptive reinforcement learning-based control system for multi-robot object transportation (2018, 17 citations) — demonstrated how autonomous robot groups can navigate obstacles while collaborating on large-scale physical tasks. Alongside this, his work on self-learning control systems (2015, 11 citations) laid important theoretical groundwork for intelligent, adaptive robotic decision-making. Diane has further advanced the field through finite-state automata-based task planning (2017, 8 citations) and hierarchical behavior scenario frameworks (2019), providing practical tools for programming autonomous robots in unpredictable settings. His more recent contributions span robotic manipulator grasping using continuous genetic algorithms, quadruped locomotion, environment mapping, and vision-based area cleanup robotics — reflecting a broad and evolving research agenda. With over 60 cumulative citations, Diane's body of work offers valuable methodologies for researchers working on real-world autonomous robot deployment and collective robotic intelligence.
Research Focus
Key Achievements
Top Papers
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- 6Hierarchical Scenarios for Behavior Planning in Autonomous Robots6 citations · 2019
- 7Multi-Aspect Environment Mapping with a Group of Mobile Robots3 citations · 2019
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- 10Network-centric motion control algorithm for a group of mobile robots3 citations · 2022