CS 326 A: Motion Planning Planning Exploration Strategies

Exploration  Map Building  A robot is introduced in a new environment  Its task is to build a model (2D or 3D) of this environment  It goes from place to place.  At each place it acquires a local model that it merges with the current global model

Example

Example

Localization Issue  Initial position of the robot  reference coordinate frame  Dead-reckoning (e.g., odometry) yields increasing errors  Need to match each new local model with current global model (correspondence problem)

Correspondence Problem

Localization Issue  Initial position of the robot  reference coordinate frame  Dead-reckoning (e.g., odometry) yields increasing errors  Need to match each new local model with current global model Simultaneous Localization and Mapping (SLAM)  Simultaneous Localization and Mapping (SLAM)

Loop Issue Courtesy R. Chatila Loops  Complex optimization problem (incrementality vs. global optomization)

Not a New Issue!

Two Sub-problems in Exploration  Where to go next? - Maximize amount of new information - Ensure minimal model overlap - Minimize travel length  Next-best view  How to best merge the two models? - Minimize expected errors: Kalman filtering, expectation maximization (most likely map)

Other Exploration Issues  Map model: polygonal, occupancy grid, topological network, 3D surfaces, object map, etc…  Moving objects  “Meaning” of objects  Multiple robots  Unstructured environments

Other Exploration Issues  Map model: polygonal, occupancy grid, topological network, 3D surfaces, object map, etc…  Moving objects  “Meaning” of objects  Multiple robots  Unstructured environments