Leng-Feng Lee (llee3@eng.buffalo) Counselor : Dr. Venkat N. Krovi Mechanical and Advanced plane design Dept. State Colle - PowerPoint PPT Presentation

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Leng-Feng Lee (llee3@eng.buffalo) Counselor : Dr. Venkat N. Krovi Mechanical and Advanced plane design Dept. State Colle PowerPoint Presentation
Leng-Feng Lee (llee3@eng.buffalo) Counselor : Dr. Venkat N. Krovi Mechanical and Advanced plane design Dept. State Colle

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Leng-Feng Lee (llee3@eng.buffalo) Counselor : Dr. Venkat N. Krovi Mechanical and Advanced plane design Dept. State Colle

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  1. Decentralized Motion Planning within an Artificial Potential Framework (APF) for Cooperative Payload Transport by Multi-Robot Collectives Leng-Feng Lee (llee3@eng.buffalo.edu) Advisor : Dr. Venkat N. Krovi Mechanical and Aerospace Engineering Dept. State University of New York at Buffalo

  2. Agenda • Motivation & System Modeling • Literature Survey & Research Issues • Local APF & limitations • Global APF-Navigation Function • Case Studies-Single robot with APF Part I • Dynamic Formulation-Group of Robots • Motion Planning-Three Approaches • Case Studies-Multi Robots with APF • Performance Evaluation of Three Approaches Part II • Conclusion & Future Work

  3. Motivation • Examples of Multi-robot groups: • Tasks are too complex; • Gain in overall performance; • Several simple, small-sized robot are easier, cheaper to built, than a single large powerful robot system; • Overall system can be more robust and reliable. • Group Cooperation in Nature: Armies of Ants Schools of Fish Flocks of Birds • How do we incorporate similar cooperation in artificial multi robot group? Introduction APF Approaches Group Robot Formulation Simulation Results Conclusion

  4. Motivation • Example of Multi robot groups: Robots in formation • Cooperative payload transport Robots in group Introduction APF Approaches Group Robot Formulation Simulation Results Conclusion

  5. Motion Planning (MP) for Robot Collectives • Definition: • The process of selecting a motion and the associated set of input forces and torques from the set of all possible motions and inputs while ensuring that all constraints are satisfied. • Why Motion Planning? • To realize all the functionalities for mobile robots, the fundamental problem is getting a robot to move from one location to another without colliding with obstacles. • MP for Robot Collective - • MP exist for individual robots such as manipulator, wheeled mobile robot (WMR), car-like robot, etc. • We want to examine extension of MP techniques to • Robot Collectives Introduction Model Distribution Analysis Manipulability Cooperative System Conclusion

  6. Motion Planning Algorithm Classification • Explicit Motion Planning: • Decompose MP problem into 3 tasks: • Path Planning, Trajectory Planning, & Robot Control; • Example: Road Map Method, Cell Decomposition, etc. • Implicit Motion Planning: • Trajectory and actuators input are not explicitly compute before the motion occur. • Artificial Potential Field (APF) Approach belongs to this category. • Combine Path Planning, Trajectory Planning, and Robot Control in a single framework. Introduction Model Distribution Analysis Manipulability Cooperative System Conclusion

  7. Motion Planning (cont’) • Artificial Potential Field (APF) Approach: • Obstacles generated a artificial Repulsive potential and goal generate an Attractive potential. • Motion plan generated when attractive potential drives the robot to the goal and repulsive potential repels the robot away from obstacles. • Combine Path Planning, Trajectory Planning, and Robot Control in one framework. Subclass of Implicit Motion Planning Algorithm Introduction Model Distribution Analysis Manipulability Cooperative System Conclusion

  8. Research Issues • Broad Challenges: • Extending APF approach for Multi-robot collectives. • Ensuring tight formations required for Cooperative Payload Transport application. • Specific Research Questions: • Which type of potential function is more suitable for MP for multi robot groups? • How can we use the APF framework to help maintain formation? and • How this framework be extended to realize the tight formation requirement for cooperative payload transport? Introduction Model Distribution Analysis Manipulability Cooperative System Conclusion

  9. Research Issues (cont’) • To answer these research questions: • Part I: • Study various APF & their limitations; • Determined a suitable APF as our test bed; • Create a GUI to design and visualize the potential field; • Case studies: MP for single robot using APF approach. • Part II: • E.O.M. for group of robots with formation constraints; • Solved the MP planning problem using three approaches; • Performance evaluation using various case studies. Introduction Model Distribution Analysis Manipulability Cooperative System Conclusion

  10. Research Issues (cont’) • Hierarchical difficulties in MP: (Dynamic Model) • Our results: • Multiple point-mass robots; • Sphere World; • Stationary Obstacles & Target. Introduction Model Distribution Analysis Manipulability Cooperative System Conclusion

  11. System Modeling • Individual level system models include: • Point Mass Robot; • Differentially Driven Nonholonomic Wheel Mobile Robot (NH-WMR). Introduction Model Distribution Analysis Manipulability Cooperative System Conclusion

  12. System Modeling (cont’) • Group level system model is formed using: • Point Mass Robot; • Differentially Driven Nonholonomic Wheel Mobile Robot. Introduction Model Distribution Analysis Manipulability Cooperative System Conclusion

  13. PART I: Artificial Potential Approach • Examine: • Variants of APF & their limitations; • Navigation function ; • Single module formulations; • Simulation studies.

  14. Local APF -background • Artificial Potential Field Approach • Proposed by Khatib in early 80’s. • FIRAS Function. [Khatib, 1986] • Later, various kind of Potential Functions were proposed: • GPF Function. [Krogh, 1984] • Harmonic Potential Function. [Kim, 1991] • Superquadric Potential Function. [Khosla, 1988] • Navigation Function.[Koditschek, 1988] • Ge New Potential. [Ge, 2000] Introduction APF Approaches Group Robot Formulation Simulation Results Conclusion

  15. Local APF Approach-Formulation • Idea: • Goal generate an attractive potential well; • Obstacle generate repulsive potential hill; • Superimpose these two type of potentials give us the total potential of the workspace. Where: denote the total artificial potential field; denote the attractive potential field; and is the repulsive potential field. is the position of the robot. Introduction APF Approaches Group Robot Formulation Simulation Results Conclusion

  16. Local APF -Attractive potential • Characteristics: • Affect every point on the configuration space; • Minimum at the goal. • The gradient must be continuous. Introduction APF Approaches Group Robot Formulation Simulation Results Conclusion

  17. Local APF -Attractive potential • Example 1: Where: = Positive scaling factor = Euclidean distance between the robot and the target = Position of the target. = Position of the robot. is commonly used. Introduction APF Approaches Group Robot Formulation Simulation Results Conclusion

  18. Local APF -Attractive potential • Example 2: Where: = Positive scaling factor For distance smaller than s, conical well. For distance larger than s, constant attractive force. Introduction APF Approaches Group Robot Formulation Simulation Results Conclusion

  19. Local APF -Repulsive potential • Characteristics: • The potential should have spherical symmetry for large distance; • The potential contours near the surface should follow the surface contour; • The potential of an obstacle should have a limited range of influence; • The potential and the gradient of the potential must be continuous. Introduction APF Approaches Group Robot Formulation Simulation Results Conclusion

  20. Local APF -Repulsive potential • Example 1 - FIRAS Function: Where: = Positive scaling factor = the shortest Euclidean distance between the robot from the obstacle surface Introduction APF Approaches Group Robot Formulation Simulation Results Conclusion

  21. Local APF -Repulsive potential • Example 2 - Superquadric Potential Function: • Approach Potential; • Avoidance Potential. • Avoid creation of local minima result from flat surface by creating a symmetry contour around the obstacle. Introduction APF Approaches Group Robot Formulation Simulation Results Conclusion

  22. Local APF -Repulsive potential • Example 3 - Harmonic Potential Function: Attractive Potential Repulsive Potential • Superimpose of another harmonic potential is also a harmonic potential. • More complicated shape can be modeled using ‘panel method’. Detail Introduction APF Approaches Group Robot Formulation Simulation Results Conclusion

  23. Local APF -Repulsive potential • Example 4 - Ge New Function: Where: = Minimal Euclidean distance from robot to the target. • Modified from FIRAS function to solve the ‘Goal NonReachable for Obstacle Nearby’ -GNRON problem. • Ensures that the total potential will reach its global minimum, if and only if the robot reaches the target where Introduction APF Approaches Group Robot Formulation Simulation Results Conclusion

  24. Local APF -Repulsive potential • Potential Function with Velocities Information: • Some potential function include the velocities information of the robots, obstacles and target. • Example: Ge & Cui Potential [Dynamic obstacle & Target]. • Provide a APF for dynamic workspace. • Example: GPF Function. [Dynamic obstacles only]. • Can be used with our formulation for group of robots for motion planning in dynamic workspace. Introduction APF Approaches Group Robot Formulation Simulation Results Conclusion

  25. Local APF –Total Potential • Total Potential of Workspace: • Superimpose different repulsive potential from obstacles and different attractive potential from the goal, we get the total potential for the workspace. • At any point of the workspace, the robot will reach the target byfollowing the negative gradient flow of the total potential. Introduction APF Approaches Group Robot Formulation Simulation Results Conclusion

  26. Local APF –Total Potential • Example: FIRAS Function Rectangular Obstacle: Circular Obstacle: Radius 2 unit in height, 1 unit in width. Target : More Introduction APF Approaches Group Robot Formulation Simulation Results Conclusion

  27. Local APF –Limitations • Local Minimum - result from single obstacle 3D View Introduction APF Approaches Group Robot Formulation Simulation Results Conclusion

  28. Local APF –Limitations • Local Minimum - result from multiple obstacles Introduction APF Approaches Group Robot Formulation Simulation Results Conclusion

  29. Local APF –Limitations • Limitation - Target close to obstacle: Introduction APF Approaches Group Robot Formulation Simulation Results Conclusion

  30. Local APF -Limitations • Some other limitations include: • No passage between closely spaced obstacle. • Non optimal path. • Implementation related limitations. • Oscillation in the presence of obstacle; • Oscillation in narrow passages; • Infinite torque is not possible. Introduction APF Approaches Group Robot Formulation Simulation Results Conclusion

  31. Global APF – Navigation Function [ Proposed by: Rimon & Koditschek] • Properties: • Guarantee to provide a global minimum at target. • Bounded maximum potential. Let be a robot free configuration space, and let be a goal point in the interior of , A map is a Navigation Function if it is: . function. , that is, at least a 1. Smooth on . 2. Polar at ,i.e., has a unique minimum at on the path-connected component of containing , i.e., uniformly maximal on the boundary of 3. Admissible on 4. A Morse Function Introduction APF Approaches Group Robot Formulation Simulation Results Conclusion

  32. Feature: Tunable by a single parameter : Navigation Function • Navigation Function of a sphere world : Where: Detail is the implicit form of bounding sphere. is the implicit form of obstacle geometric Eq. Number of obstacles Introduction APF Approaches Group Robot Formulation Simulation Results Conclusion

  33. Navigation Function • Example - Navigation Function of a sphere world : Where: Introduction APF Approaches Group Robot Formulation Simulation Results Conclusion

  34. Navigation Function -Constructions • At low value of , local minima may exist: Introduction APF Approaches Group Robot Formulation Simulation Results Conclusion

  35. Navigation Function – MATLAB GUI • A GUI to properly select a value: Introduction APF Approaches Group Robot Formulation Simulation Results Conclusion

  36. APF Approach – Formulation & Simulation • Idea: • We want the robot to follow the negative gradient flow of the workspace potential field; • Analogy to a ball rolling down to the lowest point in a given potential. • Thus the gradient information will serve as the input to the robot system. Introduction APF Approaches Group Robot Formulation Simulation Results Conclusion

  37. APF Approach – Formulation • Formulation – Single point-mass robot: Kinematic Model: Dynamic Model: is a positive diagonal scaling matrix is the gradient of the potential field is dissipative term added to stabilize the system Introduction APF Approaches Group Robot Formulation Simulation Results Conclusion

  38. APF Approach – Formulation • Formulation – Nonholonomic Wheeled Mobile Robot (NH-WMR): Kinematic Model: is the projected gradient onto the direction of forward velocity. is the proportional to the angular error between the gradient and robot direction. the desired x-direction velocity. desired y-direction velocity. Introduction APF Approaches Group Robot Formulation Simulation Results Conclusion

  39. APF Approach – Formulation • Formulation – Group robot without formation constraints: Generalize position: -number of point-mass robot Kinematic Model: Dyanamic Model: Introduction APF Approaches Group Robot Formulation Simulation Results Conclusion

  40. APF Approach – Simulations • Simulation 1 – Single robot with single obstacle: Detail Introduction APF Approaches Group Robot Formulation Simulation Results Conclusion

  41. APF Approach – Simulations • Simulation 2 – Single robot with two obstacles: Detail Introduction APF Approaches Group Robot Formulation Simulation Results Conclusion

  42. APF Approach – Simulations • Simulation 3 – Single NH-WMR with four obstacles: Detail More Introduction APF Approaches Group Robot Formulation Simulation Results Conclusion

  43. APF Approach – Simulations • Simulation 4 – Group robots without formation constraint: Detail Introduction APF Approaches Group Robot Formulation Simulation Results Conclusion

  44. APF Approach – Simulations • Simulation 5 – Group robots without formation constraint: Detail Introduction APF Approaches Group Robot Formulation Simulation Results Conclusion

  45. PART II: Group Robots Dynamic Formulation • Include: • Dynamic Formulation for Group of Robots with Formation; • Solved the E.O.M using three Methods; • Simulation Studies; • Performance evaluation of each Methods.

  46. Group Robots Dynamic Formulation • Approaches for formation maintenance: • Formation Paradigm • Leader-follower [Desai et. al., 2001] • Virtual structures [Lewis and Tan, 1997] • Virtual leaders [Leonard and Fiorelli, 2001], [Lawton, Beard et al., 2003] • Our Approaches: • View as a constrained mechanical system. • Formation constraints – holonomic constraints added to a unconstrained dynamic system. • Motion planning now can be treated as a forward dynamic simulation of a constrained mechanical system. Introduction APF Approaches Group Robot Formulation Simulation Results Conclusion

  47. Group Robots Dynamic Formulation • The dynamic of group of robot can be formulated using Lagrange Equation by: (1) is the n-dimensional vector of generalized coordinates is the n-dimensional vector of generalized velocities is the n-dimensional vector of generalized velocities is the n-dimensional vector of external forces is the vector of input forces, which is is the Jacobian matrix Introduction APF Approaches Group Robot Formulation Simulation Results Conclusion

  48. Group Robots Dynamic Formulation • The Lagrange Equation can be solved using following three methods: • Method I: Direct Lagrange Multiplier Elimination Approach. • Explicitly computing the Lagrange multiplier by a projection into the constrained force space. • Method II: Penalty Formulation Approach. • Approximating the Lagrange multiplier using artificial compliance elements such as virtual springs and dampers. • Method III: Constraints Manifold Projection Based Approach • By projecting the equations of motion onto the tangent space of the constraint manifold in a variety of ways to obtain constraint-reaction free equations of motions. Introduction APF Approaches Group Robot Formulation Simulation Results Conclusion

  49. Group Robots Dynamic Formulation • Method I: Direct Lagrange Multiplier Elimination Approach: • The direct Lagrange multiplier elimination is a centralized approach where the Lagrange multiplier is explicitly calculated to ensure formation constraints are not violated. (2) The resulting Dynamic Equation can be expressed as: (3) Detail Introduction APF Approaches Group Robot Formulation Simulation Results Conclusion

  50. Group Robots Dynamic Formulation • Method II: Penalty Formulation Approach: • The holonomic constraints are relaxed and replaced by linear/non-linear spring with dampers. • Here, the Lagrange multipliers are explicitly approximated as the force of a virtual spring or damper based on the extent of the constraint violation and assumed spring stiffness and damping constant. This can be expressed as: Resulting Dynamic Equation: (4) Introduction APF Approaches Group Robot Formulation Simulation Results Conclusion