Estimation, Search, and Planning (ESP) Research Group

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.