1. Study on Mobile Robot Navigation Techniques Presenter: 林易增 2008/8/26

2. 1.Introduction 2.Localization and Map-building 3.Local and Global Navigation 4.Command Arbitration 5.Integrated Approaches

3. 1.Introduction Goal –Reaching a destination. –Following a trajectory. –Exploring and mapping an area.

4. Subtask –Identifying the current location. –Avoiding any collisions. –Determining a path. –Resolving any conflicts between the previous two subtasks.

5. Two categories of mobile robots –Holonomic http://tinyurl.com/5d3c76 –Non-holonomic

6. 1.Introduction 2.Localization and Map-building 3.Local and Global Navigation 4.Command Arbitration 5.Integrated Approaches

7. 2. Localization and Map-building Localization –Identify the robot’s position in the environment. Map-building –construct an internal model of any unknown features in the environment.

8. Simultaneous localization and mapping (SLAM). Metric techniques. Topological techniques.

9. Cell-based techniques

12. Problem of Cell-based techniques. –Range sensors are vulnerable to false echo. –Vehicles are prone to drift and slippage.

13. Cell-based techniques The Certainty Grid

14. 2.2Topological Approaches Topological techniques rely on place recognition to localize a robot. Distinguishable place represented by node.

15. 2.2Topological Approaches 2.2.1 dropped makers at each node. 2.2.2 used the distances to obstacles as identifying characteristics. 2.2.3 using a panoramic vision system.

16. 2.2Topological Approaches 2.2.3 using a panoramic vision system. –system must be trained –Image retrieval

17. 1.Introduction 2.Localization and Map-building 3.Local and Global Navigation 4.Command Arbitration 5.Integrated Approaches

18. 3.Local and Global Navigation Local Navigation –Robots rely only on current or recent sensor data. –Avoid collision. –Tends to fail in deadlock. Global Navigation –Broader objective. –Long-range path.

19. 3.1Local Navigation 3.1.1 Rule-base 3.1.2 Artificial Potential Field (APF) 3.1.3 The Virtual Force Field (VFF) 3.1.4 the vector field histogram (VFH)

20. 3.1Local Navigation 3.1.3 The Virtual Force Field (VFF) –Differ from the certainty grid in the way it is built and updated. –Only one cell is incremented for each range reading.

21. 3.1Local Navigation 3.1.3 The Virtual Force Field (VFF)

22. 3.1Local Navigation 3.1.3 The Virtual Force Field (VFF)

23. 3.1Local Navigation Shortcoming of the VFF –Obstacles were places at least 1.8m apart. –When position changes from one cell to another.

24. 3.1Local Navigation Three levels of data representation –Highest ： identical to VFF. –Intermediate ： a one-dimensional polar array H. –Lowest ： the values for the drive and steer controllers of the vehicle.

25. 3.1Local Navigation 3.1.4 the vector field histogram

26. 3.1Local Navigation Example of an obstacle course

27. 3.1Local Navigation The corresponding histogram grid representation

28. 3.1Local Navigation

29. 3.1.4 the vector field histogram

30. 3.2Global Navigation The objective of global navigation is to find an optimal path. 3.2.1 brute-force. 3.2.2 improvement of the brute-force. 3.2.3 probabilistic techniques

31. 1.Introduction 2.Localization and Map-building 3.Local and Global Navigation 4.Command Arbitration 5.Integrated Approaches

32. 4.Command Arbitration 4.1 layerd control system 4.2 Distributed Architecture for Mobile Navigation (DAMN).

33. 1.Introduction 2.Localization and Map-building 3.Local and Global Navigation 4.Command Arbitration 5.Integrated Approaches

34. Motion problem can be considered as a single task. Challenging becomes computation intensive.