Papers
7
Total Citations
352
H-Index
6
About
Tansel Uras is a leading researcher in multi-robot coordination, task and motion planning, and cognitive robotics. His work is distinguished by a tight integration of high-level causal reasoning with low-level geometric and motion planning, a challenge he has addressed across multiple foundational papers. His 2011 paper on combining causal and geometric reasoning for robotic manipulation has garnered 126 citations, establishing a formal framework for bilateral interaction between task and motion planning. Uras has also made seminal contributions to Multi-Agent Path Finding (MAPF), with his 2017 overview on generalizing MAPF to real-world scenarios (91 citations) serving as a key reference for the field. He developed lattice-based approaches for coordinating non-holonomic vehicles (53 citations) and proposed hierarchical frameworks using simple temporal networks for multirobot plan generation and execution (38 citations). His work extends to logic-based optimal planning for robot teams and causality-based diagnostic reasoning for cognitive factories. Uras’s research is notable for bridging theoretical AI with practical robotics, addressing real-world constraints like deadlock avoidance and robot transfer between teams. His contributions have shaped how autonomous systems reason and act in complex, multi-agent environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6Causality-based planning and diagnostic reasoning for cognitive factories20 citations · 2012
- 7A tight integration of task planning and motion planning in an execution monitoring framework2 citations · 2010