DESIGN OF A GENERIC PATH PATH PLANNING SYSTEM

1. DESIGN OF A GENERIC PATH PATH PLANNING SYSTEM AILAB Path Planning Workgroup

2. OUTLINE • Path Planning Basics • Current Implementations • System Design • Conclusion AILAB Path Planning Workgroup

3. PATH PLANNING BASICS • Path • Configuration • Work Space • Configuration Space (Cspace) • Cell Decomposition • Roadmap (Skeletonization) • Free, Obstacle, Unknown Space • Dimension and Degrees of Freedom AILAB Path Planning Workgroup

4. Cell Decomposition • Regular Grids • Multiresolution Cells • Trapezoidal Cells AILAB Path Planning Workgroup

5. Roadmap (Skeletonization) • Meadow Maps • Generalized Voronoi Diagrams • Visibility Graphs • Probabilistic Roadmaps AILAB Path Planning Workgroup

6. Properties of Path Planners • Dynamic vs. static • Global vs. local • Optimal vs. suboptimal • Complete vs. heuristic • Metric vs. topological AILAB Path Planning Workgroup

7. Classification of Obstacles Category of Obstacles from Arai et. al. [Arai89, 28] AILAB Path Planning Workgroup

8. Path Planning Techniques • Reactive Methods • Artificial Potential Fields • Vector Field Histogram Method • Graph Traversing Methods • A* Algorithm • Best First / Breadth First / Greedy Search • Wavefront Method • Other Methods • Wall following, Space filling curves, Splines,Topological maps, etc. AILAB Path Planning Workgroup

9. Possible problems of applying ordinary PP methods to MAS are, Collisions, Deadlock situations, etc. Problems with MA-PP are, Computational overhead, Information exchange, Communication overhead, etc. Problems with MA-PP AILAB Path Planning Workgroup

10. Approaches • Cenralised: All robots in one composite system. + Find complete and optimum solution if exists. + Use complete information - Exponential computational complexity w.r.t # of robots - Single point of failure • Decoupled: First generate paths for robots (independently), then handle interactions. + Proportional computation time w.r.t # of robots + Robust - Not complete - Deadlocks may occur AILAB Path Planning Workgroup

11. Improvements for MA-PP • Priority assignment • Aging • Rule-Based methods • Resource allocation • Robot Groups • Virtual dampers and virtual springs • Assigning dynamic information to edges and vertices ... AILAB Path Planning Workgroup

12. Characteristics of MAS According to Dudek et. al. [Dudek96,53], • Team Size1, 2, limited, infinite • Communication RangeNone, Near, Infinite • Communication TopologyBroadcast, Addressed, Tree, Graph • Communication BandwidthHigh, Motion related, Low, Zero • Team CompositionHomogeneous, Heterogeneous AILAB Path Planning Workgroup

13. Characteristics of Domain • Initial InformationNone, Partial, Complete • Number of Targets1, Many • Target AvailableTrue (i.e. go to target), False (i.e. explore for target) • Stationary TargetsTrue, False AILAB Path Planning Workgroup

14. Complexity of Path Planning • In 3D work space finding exact solution is NP-HARD. [Xavier92, 54] • Path planning is PSPACE-HARD. [Reif79,55] • The compexity increases exponentially with, • Number of DOF [Canny88, 9] • Number of agents AILAB Path Planning Workgroup

15. Imperfect solutions • Used in case of compex problems, • Approximation • Probabilistic • Heuristic • Special cases AILAB Path Planning Workgroup

16. CURRENT IMPLEMENTATIONS • Sampling Based Algorithms • Incomplete, but efficient and practical • Types • Multiple Query • Single Query AILAB Path Planning Workgroup

17. Multiple Query • A map is generated for multiple queries • Fill the space adequately • Probabilistic Roadmap • Uniform sampling of C-free • Local planner attempts connections • Biased sampling AILAB Path Planning Workgroup

18. Single Query • Suited for high dimensions • Find a path as quick as possible • RRTs • Grow from an initial state • RRT-Connect : Grow from both initial and goal • Expand by performing incremental motions AILAB Path Planning Workgroup

19. Demos • Path Planning • Probabilistic Roadmap (PRM) • Different sampling methods • Rapidly-exploring Random Trees (RRTs) • RRT • RRT-Connect AILAB Path Planning Workgroup

20. SYSTEM DESIGN * Following slides are based on Lavelle’s Motion Strategy Library, implemented in C++ AILAB Path Planning Workgroup

21. Overview MODULES: • Model • Geom • Problem • Solver • Scene • Render • Gui AILAB Path Planning Workgroup

22. Model • Contain incremental simulators that model the kinematics and dynamics of a variety of mechanical systems. The methods allow planning algorithms to compute the future system state, given the current state, an interval of time, and a control input applied over that interval. AILAB Path Planning Workgroup

23. Geom • These define the geometric representations of all obstacles in the world, and of each part of the robot. The methods allow planning algorithms to determine whether any of the robot parts are in collision with each other or with obstacles in the world.(PQP - the ProximityQueryPackage) AILAB Path Planning Workgroup

24. Problem • This is an interface class to a planner, which abstracts the designer of a planning algorithm away from particular details such as collision detection, and dynamical simulations. Each instance of a problem includes both an instance of Model and of Geometry. An initial state and final state are also included, which leads to a problem to be solved by a solver (typically a planning algorithm). AILAB Path Planning Workgroup

25. Planner • The most important module. • Base for all path planners... AILAB Path Planning Workgroup

26. CONCLUSION • Path planning is a challenging task with many different applications. • Each application may device its own path planning strategy. • A generic path planning library may provide solution or guidelines for other path planners. • ... AILAB Path Planning Workgroup

27. QUESTIONS? Thank you... kaplanke@boun.edu.tr fuatgeleri@gmail.com AILAB Path Planning Workgroup