Mohammad Danesh
Papers
16
Total Citations
228
H-Index
8
About
Mohammad Danesh is a robotics and control systems researcher whose work spans intelligent path planning, adaptive force control, and legged and mobile robotics. Over two decades, he has made significant contributions to the field of robot manipulator control, most notably through his development of sensorless adaptive force/position control strategies. His landmark 2015 study introduced a position-based adaptive force estimator (AFE) combined with an environment compliance estimator, eliminating the need for costly force sensors in robotic manipulation — a contribution that has garnered 32 citations. His earlier foundational work on force disturbance rejection and adaptive parameter estimation (2005–2006) laid the groundwork for these advances. Danesh has also made notable strides in motion planning, with his 2018 Fuzzy Greedy RRT algorithm for complex configuration spaces becoming his most-cited work at 51 citations, reflecting growing interest in intelligent autonomous navigation. His research extends into mobile and legged robotics, encompassing Ballbot dynamics, biped trajectory generation, and parallel parking controllers. His work on biped robots incorporates ZMP stability criteria and genetic algorithm-based optimization to produce human-like gaits. With a body of work exceeding 200 total citations, Danesh represents a productive voice in autonomous robotics and intelligent control engineering.
Research Focus
Key Achievements
Top Papers
- 1Fuzzy Greedy RRT Path Planning Algorithm in a Complex Configuration Space51 citations · 2018
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- 5External Force Disturbance Rejection in Robotic Arms: An Adaptive Approach19 citations · 2005
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- 7An Adaptive Manipulator Controller Based on Force and Parameter Estimation16 citations · 2006
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