Wandering Standpoint Algorithm

Wandering Standpoint Algorithm for local path planning Description: –Local path planning algorithm. Required: –Local distance sensor. Algorithm: 1.Try to reach goal from start in direct line. 2.When encountering an obstacle, measure avoidance angle for turning left and for turning right, turn to smaller angle. 3.Continue with boundary-following around the object, until goal direction is clear again.

Variant on robot Variant with existing map or vision from ceiling –Try to reach goal from start in direct line.

Mappingalgorithms

Mapping Mapping an unknown environment is similar to the maze problem However, maze is very simple: –fixed size cells – only 90º angles Now: let us look at general environments

Mapping ideas Explore unknown environment Use infra-red PSD and infra-red proxy sensors only Apply DistBug algorithm for wall following once an obstacle is encountered Enter sensor measurement data in map Use visibility graph with configuration space representation

Exploring cells of the map – grid based Grid or no grid?

Exploring obstacles in the map - general maps, shapes, no grid.continued

This slide explains how to use grids to draw the map based on sensor information and actions executed. Grids Mapping based on Grids

Such parts can be next fixed based on general predetermined knowledge of the nature of walls, obstacles and sizes. This slide explains how to use grids to draw the map based on sensor information and actions executed.

The smaller the error the more accurate the map Fixing errors from measurements

Experimental evaluation of errors for your labyrinths

You should collect these kinds of data for your robot environment of the demo. Think in advance where our robots will be demonstrated. Deans attrium? Near elevators? Not the lab!!

DistBug Algorithm

Description: –Algorithm combining local planning with global information, guarantees convergence. Required: –Local sensor data plus global information. Algorithm: 1.Similar to wandering standpoint algorithm, –but boundary-following stops only if goal is directly reachable –or if future hit-point with next obstacle would be closer to goal. 2.This global information together with detection of unreachable goal if robot has turned 360° guarantees convergence. 3.Although this algorithm has very nice theoretical properties, it is not always usable in practice, since it requires global information in the form of path intersection points of future possible collision points with objects.

Conclusions and to think about 1.Search algorithms. 1.Search algorithms. Now that you understand one application of search, go read again the slides about search algorithms and think how they can be used in applications from last few sets of slides. 2.Fitness function. What can be the cost (fitness) functions? 3.Mapping. Think about other mapping algorithms. Can you use randomness?