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Multi-Arm Manipulation Planning (1994)

Multi-Arm Manipulation Planning (1994). Yoshihito Koga Jean-Claude Latombe. Motivation For Multi-Arm Planning. Improved efficiency through simultaneous motion Cooperate to move heavy/bulky objects Increased workspace of the moving objects by passing the object from one arm to the other.

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Multi-Arm Manipulation Planning (1994)

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  1. Multi-Arm Manipulation Planning (1994) Yoshihito Koga Jean-Claude Latombe

  2. Motivation For Multi-Arm Planning • Improved efficiency through simultaneous motion • Cooperate to move heavy/bulky objects • Increased workspace of the moving objects by passing the object from one arm to the other

  3. Problem Overview • 3D Workspace • Movable object M, with 6 degrees of freedom • Multiple robot arms working together to move M from initial to goal configuration

  4. Grasping • A grasp is a rigid attachment of the last link in an arm to M • Predefined finite grasp set set associated with M • 2 Types of movable objects considered: • Those that can be moved by a single grasping robot • Those that can be moved by 2 grasping robots

  5. Algorithm Overview • Particularly designed for problems of a complexity found in manufacturing (assembling, welding, etc.) • Problems of this type are so complicated with so many degrees of freedom that it is not feasible to search the whole configuration space • Sacrifice completeness in nasty cases by making reasonable assumptions for these types of problems • Decompose path planning process into smaller pieces

  6. Space Types • Stable space: set of all legal configurations where movable object M is statically stable • Grasp space: set of all legal configurations where at least one robot is grasping M with sufficient torque to move it

  7. Path Types • Transit path: arm motions that do not change position of M • Transfer path: arm motions that changes position of M • Manipulation Path: alteration of transit and transfer paths from initial configuration to goal configuration

  8. Algorithm Intuition • Stage 1: Plan all transfer tasks in sequence, defining exactly when and where grasp transfers of M are made • Stage 2: Fill in each transit path (start and goal configurations were exactly defined by transfer tasks)

  9. Algorithm Intuition • This decomposition greatly reduces search time • Makes assumption that some valid transit path must exist between the 2 valid endpoint configurations defined in the transfer path phase; not unreasonable for arms in 3D space

  10. Stage 1: Transfer Path Generation • Generation of a path obj that defines a path of M from start to goal such that the necessary number of arms are grasping M at all times • Might involve several changes changes of grasping arms • Uses a modified version of the RPP algorithm

  11. Modified RPP • Iteratively moves M toward the goal in small steps determined by a potential field • At each step M cannot intersect with an obstacle, and there must be some legal grasping of M

  12. Modified RPP • Maintain a set of possible grasp assignments first computed at the initial state • At each step of RPP, if any grasp in this set is no longer possible, remove it from the grasp set • If the grasp set becomes empty recompute all possible grasp assignments for that position of M; this represents a grasp change and a transit path will have to be planned

  13. Modified RPP • Assume that each arm has some predefined non-obstructive position that it can move while other arms are involved in a transfer path

  14. Stage 2: Transit Path Generation • Transfer path planning phase defines several transit path problems; assume each is solvable • First and last transit paths are easy and can be solved with a normal planning algorithm such as regular RPP • Middle transit paths (between 2 transfer paths) are harder because we must change grasps while maintaining M in a stable configuration

  15. Stage 2: Transit Path Generation • Transit task might require several regrasps to solve • Generate all grasping assignments achievable from initial configuration • Generate successors of these grasping assignments until goal grasp is achieved

  16. Simple Example

  17. Conclusion • Fast but not necessarily complete • Planned paths are good in terms of distance traveled by M and number of regrasps done in that path • Parallel processing possible

  18. Limitations/Extensions • Take advantage of stable configurations on the floor or some obstacle • Multiple movable objects • More realistic models of dynamics and torques

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