This document discusses navigation techniques for robots. It begins by reviewing localization methods like Markov localization and Kalman filters. It then discusses two approaches for navigation - local planning, which involves moving towards a goal while avoiding obstacles, and global planning, which calculates an offline shortest path. Various global planning techniques are described, including graph-based methods like Dijkstra's and A* algorithms, as well as cell decomposition and potential field planning. Potential field planning calculates a virtual force pulling the robot based on distance to obstacles and goal direction. The document concludes by discussing debates around social and technical topics related to robotics.

1. Introduction to RoboticsNavigationApril 5, 2010

2. Review: LocalizationLocalization is probabilisticError propagation lawMarkov localization and Kalman FilterSimultaneous Localization and Mapping

3. Last ExerciseBeacon-basedLine-basedMaps are used for LOCALIZATION

4. Today: NavigationHow to find a collision-free, shortest path from A to B?Two approaches:Local planning: go towards goal while avoiding obstaclesGlobal planning: calculate shortest path offline

5. Global PlanningWorkspaceConfiguration Space

6. Graph-based and Potential-field PlanningGrid-decompositionVisibility GraphPotential field

7. Configuration SpaceGrow obstacles at least by radius of robot

8. Voronoi Decomposition

9. Exact Cell Decomposition

10. Adaptive Cell Decomposition

11. Graph-based planningDijkstra/WavefrontA*

12. Rapidly Exploring Random TreesSelect a random point in the configuration spaceGrow tree into this direction from the closest point already in the graphExplores space quickly, and eventually completely

13. Potential-field based PlanningPotential given by Distance to obstaclesDirection to goalPossible to construct more complex behaviors

14. Calculate virtual force pulling at the robotDifferential wheel robotLeft wheel = Fx – FyRight wheel = Fx + FyPotential-Field based Planningxy

15. Reactive Obstacle AvoidanceGoalBraitenberg behavior not sufficient (U-obstacle)Classic: bug-algorithmsEasy to construct sub-optimal results

16. Vector Field Histogram

17. PracticeLocalization, actuation and obstacles are uncertainCombination of Local and Global Techniques

18. Debate OutlineConstructive speeches10 minutes pro10 minutes contraRebuttal 3 minutes affirmative3 minutes negativeDiscussion and cross examination5-10 minutes4 Debates total

19. DebatesSocial:Robots putting humans out of work is a risk that needs to be mitigated.Robots should not have the capability to autonomously discharge weapons.Robotic cars should not be allowed to participate in urban traffic.…Technical:Swarms of simple robots are more attractive than monolithic, more capable robots.Robots do not need to be as cognitive as humans in order to be useful as making the environment intelligent is sufficient.Robots need to be made differently than from links, joints, and gears in order to reach the agility of people.…In both cases: debates should be driven by verifiable, technical arguments!

20. DebatesSocial:D1: Robots putting humans out of work is a risk that needs to be mitigated.D2: Robots should not have the capability to autonomously discharge weapons / drive around in cities (autonomous cars).Technical:D3: Robots do not need to be as cognitive as humans in order to be useful as making the environment intelligent is sufficient.D4: Robots need to be made differently than from links, joints, and gears in order to reach the agility of people.…In both cases: debates should be driven by verifiable, technical arguments!

21. Random assignments

22. OrganizationWeek 12 + 13: Debateshttp://courses.csail.mit.edu/6.141/spring2009/pub/debates/Debates.htmlWeek 14: Graduate student presentationsWeek 15: Final presentationsFinal exam: Monday, May 3 7:30 p.m. - 10:00 p.m.