Kalin Gochev

California University of Pennsylvania, University of Pennsylvania

Papers

Total Citations

H-Index

About

Kalin Gochev is a leading researcher in robotic motion planning, specializing in algorithms that make high-dimensional path planning computationally tractable. His most influential work, "Path Planning with Adaptive Dimensionality" (2021, 37 citations), introduces a novel approach that dynamically reduces the complexity of planning for robotic arms by exploiting benign regions of the environment where lower-dimensional models suffice. This builds on his earlier foundational paper, "Incremental Planning with Adaptive Dimensionality" (2013, 20 citations), which pioneered the concept of switching between high- and low-dimensional state spaces during planning to balance speed and accuracy. Gochev has also made significant contributions to motion planning for humanoid robots, notably in "Motion planning for robotic manipulators with independent wrist joints" (2014, 12 citations), where he leveraged the independent control of wrist degrees of freedom to simplify complex manipulation tasks. Additionally, his work on heterogeneous multi-robot systems, "Planning for a ground-air robotic system with collaborative localization" (2016, 12 citations), addresses robust navigation in GPS-denied environments for search and rescue. With over 80 total citations, Gochev’s adaptive dimensionality framework has become a key tool for enabling real-time motion planning in high-dimensional robotic systems.