Amir Salimi Lafmejani
Papers
16
Total Citations
289
H-Index
8
About
Amir Salimi Lafmejani is a robotics researcher whose work spans parallel mechanisms, bio-inspired robots, and multi-robot systems. His key contributions include trajectory tracking control for pneumatically actuated Gough–Stewart platforms using Backstepping-Sliding Mode controllers, and kinematic modeling for octopus-inspired hyper-redundant robots—where each segment is modeled as a 6-DOF parallel platform, enabling unprecedented replication of octopus-like motion. In multi-robot navigation, he has advanced nonlinear model predictive control (NMPC) with learned barrier functions for decentralized, collision-free, and deadlock-free coordination among nonholonomic mobile robots, with applications in unknown and dynamic environments. His work on fully decentralized control for collective transport in microgravity targets space applications like on-orbit assembly. With over 250 citations across his top ten papers, his research has been published in leading venues including *IEEE Robotics and Automation Letters*. Notably, his fish-inspired robot achieves 0.71 body lengths per second with near-zero turning radius, and his dimensional synthesis of four-bar linkages via PSO-Cooperative Neural Networks demonstrates versatility across robotic domains.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10