The Estimation, Search, and Planning (ESP) research group is focused on improving our understanding of robotic fundamentals. We use this understanding to develop and deploy theoretically well-founded solutions to academic and real-world robotic problems.

We are primarily interested in problems arising in state estimation, task scheduling and search, and motion and path planning but we study any topic in robotics. We use a knowledge-driven research approach that is based on theoretical rigour and experimental validation and is often done with the help of external collaborators. To find out more about our research, please read about our individual work or contact us.


State estimation is the problem of measuring the position and orientation (i.e., the state) of a robot and/or its surroundings. It may also include estimating the rate of change of these variables (i.e., velocities, accelerations, etc.). It is challenging because these variables often cannot be measured directly and instead must be estimated from noisy sensors such as cameras and lidar. Getting accurate estimates from this data allows robots to operate in complex worlds.


Path planning is the problem of moving a robot between specified positions (i.e., from a start to a goal) while avoiding obstacles. It often makes use of concepts from optimization and controls (e.g., dynamic programming) and graph algorithms (e.g., A*). It is challenging because finding safe paths through complex environments is often computationally expensive. Planning these paths quickly and reliably is a necessary component of any autonomous robotic system operating in dynamic or unknown worlds.


Many of our past and present research projects are supported by external agencies and/or represent collaborations with colleagues from other disciplines or institutions.

  • Wytham Woods
  • CMU