1. Path Planning for Articulated Rovers using Fuzzy Logic and Genetic Algorithm Mahmoud Tarokh Intelligent Machines and Systems (IMS) Lab California State University-San Diego “In the next 30 years robots will impact our lives much the same way as personal computers did in the last 30 years.” Bill Gates Talk Outline: - Brief review of IMS Lab projects - Path Planner Intelligent Machines & Systems Lab

2. Intelligent Machines and Systems (IMS) Lab San Diego State University Mahmoud Tarokh Robotic Helicopter Intelligent Machines & Systems Lab

3. Security Robots Implementation of a Team of Autonomous Cooperative Security Robots for Securing Sensitive Environments. Features: Software agent paradigm Security-related activities Autonomy Cooperation Threat response N-numbered robots Robust Expandable/upgradeable design Intelligent Machines & Systems Lab

4. Kinematics Modeling and Analysis of High Mobility Rovers High mobility rovers have sophisticated suspension system to enable motion over rough terrain. Kinematics modeling is highly complex Little work done in kinematics modeling Full kinematics model and analysis of articulated rovers. Goal: To develop a systematic and universal approach to kinematics modeling Intelligent Machines & Systems Lab

5. Simulation, Animation and Visualization. Simulation Program rover.mat Desired Terrain Desired Trajectory Animation Package current_trajectory.mat current_terrain.mat 3D Representation of Rover-Terrain Interaction Model  Terrain Drawing  Trajectory Drawing Intelligent Machines & Systems Lab

6. Animation Package. Intelligent Machines & Systems Lab

7. Autonomous Agile Rover Projects Low cost agile rover to traverse rough terrain Koli Very low cost Easy to experiment Short autonomous traversal (3-5 miles) Yaboo Rough terrain Medium autonomous traversal (15-25 miles) Better suit of sensors Intelligent Machines & Systems Lab

8. Koli Design and Construction Mechanics Electronics Software Intelligent Machines & Systems Lab

9. Electronics.

10. OS: WinXP Embedded on a mini-ITX 1.2GHz with 512MB RAM Navigation System: Kalmann Filter, Firmware Vision: Obstacle Recognition, Road Following Control: High Level Behavior, Position and Speed Control Software and Control

11. Yaboo Design and Construction An ATV is converted into an autonomous rover by keeping only base and engine and replacing steering, accelerator, etc. by actuators, sensors and computer controlled devices. The system is equipped with cameras, GPS, etc.

12. Testing GPS System on Yaboo Intelligent Machines & Systems Lab

13. Intelligent Machines and Systems (IMS) Lab Robotic Helicopter Intelligent Machines & Systems Lab

14. Fuzzy Logic and Genetic Path Planner Goals: Devise a planner for a high mobility rover traversing rough terrain Issue: o Characterize terrain roughness o Search in an environment whose characteristics are roughly known o Take into consideration rover rock climbing ability o Optimize path (e.g. for min energy, etc.) o Include adaptation and learning capabilities for improved performance Intelligent Machines & Systems Lab

15. Intelligent Machines and Systems (IMS) Lab Robotic Helicopter Intelligent Machines & Systems Lab

16. Inverse Kinematics for Animation and Robotics: Decomposition, Classification and Approximation Goals : Devise and extremely fast methods of IK solution. Intelligent Machines & Systems Lab

17. IK Solutions Classifications Intelligent Machines & Systems Lab

18. Robotic Helicopter for Estuaries Goals : To monitor natural reserves and wild life To obtain images with georeference from inaccessible locations To sample water in remote areas Intelligent Machines & Systems Lab

19. A Genetic Robot Path Planner with Fuzzy Logic Adaptation Mahmoud Tarokh Intelligent Machines and Systems Laboratory Department of Computer Science San Diego State University San Diego, CA 92182, U.S.A.

20. Genetic Path Planning with Fuzzy Adaptation

21. Rocky 7, another view

22. Objectives of the Genetic Path Planner Devise a planner for a high mobility rover traversing over rough terrain Issues Characterize terrain roughness Search in an environment whose characteristics are only approximately known. Take into consideration rover rock climbing ability. Optimize the path in some desired sense (e.g. low energy consumption). Adaptability & learning capability to improve the performance.

23. Global and Local Planners Global off-line planning - Mast is raised, images of the terrain in front of rover is taken by two cameras. - Planning is based on this image Local on-line Replanning - In case sensors detect previously unaccounted obstacles, part of path is replanned.

24. Terrain Roughness Divide terrain into regions or cells. Roughness of each cell depends on: Height of rocks in the cell Size (surface area) of cell occupied by rocks Concentration (number of rocks) in the cell Slope of terrain Texture of terrain

25. Extracting terrain characteristics from images Use images taken by the rover. Apply auto-thresholding to segment image into rocks and background. Determine size of rocks in each cell. Form a contour map of the terrain.

26. Terrain image and contour map

27. Fuzzy logic Terrain Description: To cope with imprecision and ambiguity involved in the extraction of terrain information from images Fuzzifier Rock height Rock size Inference Engine Defuzzifier Terrain Roughness

28. Linguistic Terrain Description Membership function of the rock size TI SM MD LG XL Rock Size (Cm2) 36001800 Membership grade 1.0

29. Fuzzy Rule Matrix - Terrain Roughness. VL LOMDVH VL LOMDVH VLLO VH VLLOMDLOVH VLLOMDLOVH Height Size VLLOMDHIVH TI SM MD LG XL

30. Terrain Contour and Roughness Map

31. Path Representation Path represented by sequence of way points Attributes of a path cell: - Roughness - Curvature (d/D) - Slope Cell impedance:Roughness+Curvature+Slope Wi+1 Wi Wi-1 d D

32. Traversability A cell with impedance > a threshold becomes intarversable. Threshold value depends on rover mobility characteristics Path impedance is sum of cell impedances A path with one or more intraversable cells becomes an intraversable path. Intraversable paths are not discarded in the genetic process. Any travresable path is given priority over best intraversable path.

33. The Genetic Planner Create several random path between start and goal. These generally go through rough or impassable regions. Genetic operators are applied randomly to selected paths to improve path quality. Path quality is measured by path impedance. After a genetic operator is applied, the highest impedance path is discarded.

34. Genetic Operators Crossover Mutation Smoothing Replacement

35. More Genetic operators Adaptation Initially a probability is assigned to each operator. This is adjusted based on population characteristics. Swap Pull-out

36. Diversity-Traversability Diversity: Essentially standard deviation between population paths. Traversability index = N T /(N I +1) = (No. Traversable)/(No. Intraversable +1)

37. Adaptation Scheme Crossover Pull-out Crossover Smooth Pull-out ReplaceSmooth Replace Mutate Swap Pull-out Replace Smooth Replace  Traversability Diversity ^ |

38. Adaptation of Genetic Operators.

39. Convergence to Traversable Paths

40. Initial Random Paths, and Intermediate Evolved Paths

41. Fuzzy logic Terrain characterization Genetic Algorithm Path Planning Intelligent Machines & Systems Lab

42. Other Examples

43. Finding Alternative Paths

44. On-line Local Replanning In case of previously undetected obstacles: - Two maneuvering around strategy

45. Animation of Path Following

46. Path Following Animation

47. Conclusions Concepts from intelligent systems are applied to design and implement a path planner for rovers operating in rugged terrains. Terrain roughness is determined using a fuzzy login paradigm. Genetic path planner consists of global offline and local online parts. Global planner uses image of the environment, a genetic algorithm and adaptation. Local re-planner uses on-line sensory information in case unaccounted for obstacles are sensed. Results show the effectiveness of the fuzzy-genetic path planner.