Rajeev Sharma

About

Rajeev Sharma is a prominent robotics and computer vision researcher whose work has fundamentally advanced the fields of visual servoing, robot motion planning, and augmented reality. Over a career spanning more than a decade of influential publications, Sharma has made particularly notable contributions to the problem of vision-based robot control, developing quantitative measures of "motion perceptibility" and "observability" that elegantly connect visual sensing capabilities with robotic manipulation — work that has garnered over 75 and 28 citations respectively. His pioneering research on calibration-free visual servoing addressed a critical practical barrier to real-world deployment, introducing dynamic image Jacobian estimation techniques that made vision-guided systems far more robust and accessible. Equally significant is Sharma's sustained investigation into robot motion planning under uncertainty. His probabilistic frameworks for navigating dynamic, partially predictable environments — developed across multiple influential papers accumulating well over 100 combined citations — provided the field with rigorous theoretical foundations and practical computational tools. His neural network-based approach to pneumatic robot planning further demonstrated his commitment to bridging learning and sensing. Later work exploring augmented reality for assembly guidance showcased his versatility, extending his vision expertise toward human-robot collaboration. Across these domains, Sharma's research remains essential reading for students and practitioners in intelligent robotics.

Research Focus

Key Achievements

Top Papers

1. 1
   Motion perceptibility and its application to active vision-based servo control

   75 citations · 1997
2. 2
   On Motion Planning in Changing, Partially Predictable Environments

   55 citations · 1997
3. 3
   Image based autodocking without calibration

   41 citations · 2002
4. 4
   Motion planning of a pneumatic robot using a neural network

   33 citations · 1997
5. 5
   Locally efficient path planning in an uncertain, dynamic environment using a probabilistic model

   33 citations · 1992
6. 6
   On the observability of robot motion under active camera control

   28 citations · 2002
7. 7
   The role of exploratory movement in visual servoing without calibration

   27 citations · 1998
8. 8
   A framework for motion planning in stochastic environments: modeling and analysis

   16 citations · 2002
9. 9
   Computer vision and augmented reality for guiding assembly

   12 citations · 2002
10. 10
    A framework for motion planning in stochastic environments: applications and computational issues

    12 citations · 2002

Key Collaborators

Contact & Links