Discovering Environmental Structure with Mobile Robots Rahul Biswas CS326A Class Project

Introduction Traditional mapping algorithms slow Details of environment often not useful Want algorithm to find essential structure Robot must act autonomously

Related Work Planar maps mostly unexplored Focus on very simple environments Thrun et. al. have build 3D planar maps Robot not autonomous Multiple laser range finders for gathering data One hallway

Challenges Finding walls quickly and with less data Only one range finder Navigating in an unknown environment Move rapidly exploiting partially known structure Optimal exploration of unknown environment Minimize time for exploration Attain good coverage

Finding Walls Algorithm: Sample points and nearest neighbors Use PCA to fit line to this set of points Assign points near wall to wall (allow sharing) Recalculate line based on all points Extend wall while point density high Discourage walls near existing walls Find walls incrementally as robot moves

Problems with Walls Walls used to initialize poorly: Small yet important walls hard to find Walls viewed repeatedly oversampled EM-based approach highly unreliable Poor map makes navigation impossible

Exploration and Navigation Three-part planner: Explorer – decides where to go Navigator – plans path for robot to get there Controller – controls robot itself Unsolvable problems sent up hierarchy Controller has predilection for observation

Explorer Finds maximal unexplored rectangle Proceeds to center of rectangle Pseudo-quadtree maintains unexplored region Knows boundaries of building

Explorer Discards regions if A* fails to find a path Goes to least-recently seen area afterwards New goal if rectangle shrinks as robot moves Problems with exploration: Hard to know when region is inaccessible Not cognizant of sources of navigation difficulty

Navigator PRM-based approach Sampling points: Prefer points farther away from walls Add points on an as-needed basis Allows points in unexplored regions Sample, connect points quickly in closed form A* used for search Heuristic minimizes clearance

Navigator Tries to back up if initial point unconnectable Regenerates plan if map changes dramatically Increases clearance Reduces collisions Leads to indecisiveness Controller Uses small steps Rotates towards heading Moves forward

Limitations Not robust against sensor noise Makes finding walls very hard Others use scan matching to pre-filter noise Tested only in simulation Navigation independent of mapping Mapper cannot control navigation Lack of integration can get robot stuck