1. Mobile Robots Automated Guided Vehicles (AGVs) Autonomous Planes
Wheeled
Autonomous Planes
Model planes!
Autonomous Underwater Vehicles (AUVs)
Submarine
Balancing Robots
Inverted pendulum
Double inverted pendulum
Legged
Walking Robots
Multi-pedal
Bipedal

2. AGV Designs Drives Two main configurations Single wheel drive
One wheel does driving and steering
Synchro drive
Extension of single wheel drive to more than one wheel with each doing driving and steering but still only two motors
Differential drive
Two driven wheels, one on each side, allow differential steering
Tracked robots
A special case of differential drive with caterpillar tracks instead of wheels
Ackermann steering
Rear wheels drive and front do steering
Two main configurations
Tractor pulling trucks
Load-bearing vehicle

3. AGV Navigation A mobile robot constitutes a moving frame of reference
Position and orientation are determined with respect to some fixed frame of reference
Three ways to provide the fixed frame of reference
Off-Board Fixed Paths
On-Board Programmable Paths
On-Board Mapping

4. Off-Board Fixed Paths Wire guided Painted line guided Most widely used
Buried cable loops each carrying a different a.c. frequency
AGV follows a sequence of frequencies which take it from one loop to another
Can navigate from a start point to an end point via the loops
Painted line guided
Similar idea to wire guided
Lines painted with different fluorescent dyes
Photosensors used to identify the different wavelengths
Easier to modify than wire guided

5. On-Board Programmable Paths
These permit AGVs to be free-ranging rather than having to follow prescribed routes
Techniques used include
Dead reckoning
Optical encoders on drive shafts
Reference beacons
On-board device continuously monitors position w.r.t. beacons
Inertial navigation
Gyroscopes
Imaging
Cameras

6. On-Board Mapping Maps created from
Optical data
Ultrasonic data
AGV uses its sensors to locate itself and guide it towards a goal position
Map data structure
Balance needed between accurate representation of tricky parts of environment and efficiency of representation for bland parts
Route finding and path planning algorithms needed
Navigation methods
Landmark navigation
Place recognition

7. Ideal Map Data Structure
The ideal data structure for a map
should -
1. Support both global and local maps
2. Maximise accuracy of environment
3. Store complex shapes efficiently
4. Store homogeneous space efficiently
5. Minimise amount of heterogeneous
(ambiguous) space
6. Facilitate traversal between map
areas representing adjacent real
world spaces
7. Provide parsimonious data structures
for storing fixed paths
8. Support efficient algorithms for path
planning, route navigation, etc.

8. Map Representations Free-space maps Object-space maps
Spatial graphs
Voronoi diagrams
Delaunay triangulation
Generalised Voronoi diagrams
Object-space maps
Vertex graphs
Composite-space maps
Area grids
Point grids
Quadtrees

9. Map Creation Example
Consider the following floor plan -

10. Free-Space Map Creation I
We first create the spatial graph of the sensing points -

11. Free-Space Map Creation II
Next we create the Voronoi diagram of the spatial graph -

12. Free-Space Map Creation III
Alternatively we can perform a Delaunay Triangulation -

13. Free-Space Map Creation IV
Then we can create the Generalised Voronoi diagram -

14. Object-Space Map Creation
Vertex graphs can be used to build the map
A vertex graph can be as simple as a linked list of vertex co-ordinates
We first form ordered lists of vertices for each object
Then we form a list of objects
I.e. a list of lists of co-ordinates
The number of vertices that represent an object can be increased or decreased to obtain the required resolution

15. Composite-Space Map Creation I
Area grids
Point grids

16. Composite-Space Map Creation II
Quadtrees break a 2D area down by quarters
Form a tree with four children to each node
If the area represented by a child is all free space label it F, if it is all object space label it O, then stop
Otherwise quarter it further

17. Path Planning A shortest path problem
The map is used to generate possible paths from start to goal
An algorithm such as A* is then used to select the shortest path
We shall look at how
Free-space
Object-space
Composite-space
Multi-resolution
mapping systems can be used in
path planning for mobile robots

18. Free-Space Planners
Voronoi diagram identifies paths by a succession of regions labelled “clear”
Generalised Voronoi diagram identifies paths along the edges of the regions
Both approaches direct the AGV down the centre of free- space corridors which is not efficient
Piano-mover’s problem

19. Object-Space Planners
Paths are a series of vectors connecting vertices in the vertex graph
We shrink the AGV to a point and expand the objects by the AGV diameter
We seek the shortest path made up of vertices in the vertex graph which gets to the goal
Problems are
Paths pass close to corners
Paths run along object edges
Adding tolerances to objects causes further problems

20. Composite-Space Planners
Simplest use regular grids
Area grids yield centre points which become the ends of path segments
All grid planners suffer from 2 big problems
Missed paths
Crooked paths
A grid spacing equal to the AGV’s size in an 8-connected grid can mitigate against missing paths but large storage requirements result
Grid points can occur in corridors too narrow for the AGV
Crooked paths need path relaxation

21. Quadtrees and other Multi-Resolution Planners
AGV shrunk and objects expanded then resulting space described by quadtree
Leaf nodes containing Start and Goal positions are identified
Minimum cost path from S to G found with A*, etc.
Cost is a function of
Distance travelled (minimise)
Object clearance (maximise)
Adjacency information not stored in a quadtree so a complete search of the leaf nodes is needed

22. Task Planning and Manipulators
Gross motion planning -
Collision free movements in space
Comprised of
Path planning
Selecting the route to take
Trajectory planning
Constrained by
Time
Mechanics
Computation
Fine motion planning -
Possibility of damaging contact
Accurate models
Compliant motion
Sensor-based

23. Compliant Motions Touching Inserting Pushing Grasping
E.g. Placing a peg in a hole
Slide peg along surface until it drops into hole
Pushing
E.g. Picking up a thin object
Slide gripper up to object and push it until it aligns in gripper
Grasping
Ensuring sensible grasp points for both the start and end of the transfer