Wandering
Standpoint
Algorithm
Wandering Standpoint Algorithm
for local path planning
•
Description:
–
•
Required:
–
•
Local path planning algorithm.
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.
Mapping
algorithms
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
Grid or no grid?
Exploring cells of the map – grid based
continued
Exploring obstacles in the map - general maps,
shapes, no grid.
Mapping based on Grids
This slide explains how to use grids to draw the map based on sensor
information and actions executed.
This slide explains how to use grids to draw the map based on sensor
information and actions executed.
• Such parts can be next fixed based on general
predetermined knowledge of the nature of walls,
obstacles and sizes.
Fixing errors from measurements
The smaller the error the more
accurate the map
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
DistBug Algorithm
•
Description:
–
Algorithm combining local planning with global information,
•
•
Required:
–
•
guarantees convergence.
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. 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?