Vahid Salehi

About

Vahid Salehi is a robotics and automation researcher whose work sits at the intersection of artificial intelligence, autonomous systems, and industrial manufacturing. His research focuses primarily on developing intelligent frameworks for autonomous robots, with particular emphasis on AI-driven motion control, reinforcement learning, and digital twin-based development methodologies for industrial environments. Salehi's most influential contribution — his 2023 paper on the Munich Agile Concept for Model-Based Systems Engineering (MBSE) applied to Automated Guided Vehicles using Digital Twin technology — has garnered 23 citations, reflecting growing industry interest in holistic, simulation-driven robotics development. His foundational Q-Model methodology (2020, 14 citations) introduced a structured AI-based workflow for autonomous robot development, addressing the critical challenge of standardizing machine learning integration in cyber-physical systems. Across his body of work, Salehi has consistently tackled pressing practical problems: safe AI learning for depalletization robots, anomaly detection in dynamic industrial settings, intelligent gripping point detection, and perception-based material handling in logistics environments. Collectively accumulating over 80 citations, his research offers meaningful contributions to the emerging field of intelligent industrial automation, making his work particularly valuable for engineers and researchers designing the next generation of flexible, AI-powered robotic systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Application of Munich Agile Concept for MBSE Based Development of Automated Guided Robot Based on Digital Twin-Data

   23 citations · 2023
2. 2
   Q-Model: An Artificial Intelligence Based Methodology for the Development of Autonomous Robots

   14 citations · 2020
3. 3
   Ai Motion Control – A Generic Approach to Develop Control Policies for Robotic Manipulation Tasks

   10 citations · 2019
4. 4
   AI-Based Learning Approach with Consideration of Safety Criteria on Example of a Depalletization Robot

   8 citations · 2019
5. 5
   Usage Identification of Anomaly Detection in an Industrial Context

   7 citations · 2019
6. 6
   Design of a Deep Post Gripping Perception Framework for Industrial Robots

   6 citations · 2020
7. 7
   Perceptionbased Intelligent Materialhandling in Industrial Logistics Environments

   4 citations · 2019
8. 8
   Unsupervised Pose Anomaly Detection for Dynamic Robotic Environments

   4 citations · 2020
9. 9
   Robust Framework for intelligent Gripping Point Detection

   3 citations · 2019
10. 10
    Reinforcement Learning Lifecycle for the Design of Advanced Robotic Systems

    3 citations · 2020

Key Collaborators

Contact & Links