Ali Mohades
Papers
8
Total Citations
297
H-Index
6
About
Ali Mohades is a leading researcher in robotics and artificial intelligence, whose work has fundamentally advanced the fields of multi-robot coordination and autonomous navigation. His primary research areas include multi-objective path planning, swarm intelligence, and combinatorial filter optimization. Mohades is best known for pioneering multi-objective path planning algorithms that simultaneously optimize for path shortness, safety, and smoothness—a critical challenge in real-world robotics. His highly cited 2012 paper on "Multi-objective path planning in discrete space" (91 citations) laid the groundwork for this approach, while his 2018 work on Multi-Robot MOPSO (75 citations) extended these principles to unknown environments using particle swarm optimization. With over 297 total citations across his most influential works, Mohades has also made significant contributions to motion planning with Hopfield neural networks and combinatorial filter reduction, a problem of increasing importance in robotics. His 2015 paper on "Clear and smooth path planning" (65 citations) further demonstrates his sustained impact on developing practical, implementable solutions for autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1Multi-objective path planning in discrete space91 citations · 2012
- 2
- 3Clear and smooth path planning65 citations · 2015
- 4
- 5An optimal algorithm for two robots path planning problem on the grid16 citations · 2013
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- 8Improper Filter Reduction2 citations · 2017