EXAMPLES OF
PRACTICAL
ALGORITHMS
FOR PATH
PLANNING
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Bug Algorithms - Embedded Intelligent Robotics - Lecture Slides, Slides of Robotics
Course title is Embedded Intelligent Robotics. This course is for Electrical engineering students. Though good thing is everyone can learn about robotics in this course. This lecture includes: Bug Algorithms, Noncontact Sensors, Probabilistic Roadmaps, Preprocessing Phase, Roadmap Construction, Roadmap Expansion, Expansion Step, Local Planner
Typology: Slides
2013/2014
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EXAMPLES OF
PRACTICAL
ALGORITHMS
FOR PATH
PLANNING
Bug Algorithms
Bugs 2
qinit qtarget M-line Lj Hj New leave point condition: d<d(Hj,Target)
qinit qtarget M-line Lj Hj New leave point condition: d<d(Hj,Target)
1. From point Lj- 1 move along M-line until: a. Target is reached. Stop _b. An obstacle is hit at Hj. Goto 2
- Turn left and follow the boundary until:_ a. Target is reached. Stop b. M-line met at distance d from target such that: d < dist(Hj,qtarget) Define Lj, set j=j+1, and goto 1. c. If we return to Hj without meeting the M-line, stop. The target is trapped.
- Bug
- A Hard Example for Bug
- A Hard Example for Bug
Bug + Non Contact Sensor
Probabilistic Roadmaps
Description of Probabilistic Roadmap Algorithm
1. Roadmap = undirected graph R = ( N, E )
2. N : (nodes) set of selected configurations in C _free
- E_ : (edges) collection of simple paths. The Local Paths
- Local paths are computed by the fast but not powerful local planner
- Idea: connect q init and q goal with q init ’ and q goal ’ in N
- Search R for a path
Preprocessing Phase
Three stages
1. Roadmap construction. Objectives:
a) Obtain reasonable connected graph b) Be sure “difficult regions contain a few nodes
2. Roadmap expansion. Objectives:
Improve graph connectivity by selecting nodes of R which lie in (heuristic) difficult regions and adding nodes there
3. Roadmap Component reduction. Optional.
Attempts to simplify the graph
Create random configurations
AS a first step we create random configurations in the space. The algorithm creates the points one at a time, but we’re not going to do that with the slides. As configurations are created we try to connect to already existing nodes in the graph (if they are close enough they will get connected)
Update Neighboring Nodes’
Edges
As edges are added to the graph we start forming connected regions.
Expansion Step
The nodes are added and will be connected again using the local planner on the closest nodes.
End of Expansion Step
At the end of the expansion step we end up with the same number, or fewer connected components. docsity.com