Wandering Standpoint Algorithm - Embedded Intelligent Robotics - Lecture Slides, Slides of Robotics

Download Wandering Standpoint Algorithm - Embedded Intelligent Robotics - Lecture Slides and more Slides Robotics in PDF only on Docsity!

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.

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

Exploring cells of the map – grid based

Grid or no grid?

Exploring obstacles in the map - general maps,

shapes, no grid.

continued

• 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

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!! docsity.com

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. 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?