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Estimation, Search, and Planning (ESP) Research Group


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.


Search or scheduling is the problem of finding a sequence of actions that allows a robot to achieve its goal. It may have to consider a discrete set of subtasks (e.g., assembly) or a continuous space of possible actions (e.g., exploration). It is challenging because the individual actions may be poorly defined, mutually exclusive, and/or have uncertain results. Finding efficient and robust action sequence allows robots to achieve their objectives even in the presence of resource constraints and uncertainty.


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.