Manipulator Planning

An Integrated Approach to Inverse Kinematics and Path Planning for Redundant Manipulators
Dominik Bertram, James Kuffner, Ruediger Dillmann, Tamim Asfour  (ICRA '06)

http://www.kuffner.org/james/papers/unified_IK_planning_icra2006.pdf

We propose a novel solution to the problem of inverse kinematics for redundant robotic manipulators for the purposes of goal selection for path planning. We unify the calculation of the goal configuration with searching for a path in order to avoid the uncertainties inherent to selecting goal configurations which may be unreachable because they currently lie in components of the free configuration space disconnected from the initial configuration. We adopt workspace heuristic functions that implicitly define goal regions of the configuration space and guide the extension of Rapidly-exploring Random Trees (RRTs), which are used to search for these regions. The algorithm has successfully been used to efficiently plan reaching and grasping motions for a humanoid robot equipped with redundant manipulator arms.


Pose-Constrained Whole-Body Planning using Task Space Region Chains
Dmitry Berenson, Joel Chestnutt, Siddhartha S. Srinivasa, James J. Kuffner, Satoshi Kagami  (Humanoids '09)
http://www.ri.cmu.edu/pub_files/2009/12/humanoids09.pdf

We present an efficient approach to generating paths for humanoids and other robotic manipulators that uses the Task Space Region (TSR) framework to specify manipulation tasks. TSRs can define acceptable goal poses of an end-effector or constraints on the end-effector's pose during the path, or both. First presented as a method for goal-specification, TSRs are a straightforward representation of sets of end-effector poses which can be sampled and which entail a clear distance metric. This makes TSRs ideal for sampling-based motion planning. However, a finite set of TSRs is sometimes insufficient to capture the pose constraints of a given task. To describe more complex constraints, we present TSR Chains, which are defined by linking a series of TSRs. Though the sampling for TSR Chains follows clearly from that of TSRs, the distance metric for TSR Chains is radically different. We also present a new version of our Constrained Bidirectional RRT (CBiRRT2) planner, which is capable of planning with TSR chains as well as other constraints. We demonstrate our approach on the HRP3 robot by performing a variety of whole-body manipulation tasks.


Manipulation Planning on Constraint Manifolds
Dmitry Berenson, Siddhartha Srinivasa, David Ferguson, and James Kuffner

http://www.ri.cmu.edu/pub_files/2009/5/icra09.pdf

We present the Constrained Bi-directional Rapidly- Exploring Random Tree (CBiRRT) algorithm for planning paths in configuration spaces with multiple constraints. This algorithm provides a general framework for handling a variety of constraints in manipulation planning including torque limits, constraints on the pose of an object held by a robot, and constraints for following workspace surfaces. CBiRRT extends the Bi-directional RRT (BiRRT) algorithm by using projection techniques to explore the configuration space manifolds that correspond to constraints and to find bridges between them. Consequently, CBiRRT can solve many problems that the BiRRT cannot, and only requires one additional parameter: the allowable error for meeting a constraint. We demonstrate the CBiRRT on a 7DOF WAM arm with a 4DOF Barrett hand on a mobile base. The planner allows this robot to perform household tasks, solve puzzles, and lift heavy objects.


Planning of Graspless Manipulation based on Rapidly-Exploring Random Trees
Miyazawa, Maeda, Arai
http://www.iir.me.ynu.ac.jp/~maeda/research/IEEE/ITP3.PDF

Planning of graspless manipulation is a difficult problem because of the complicated mechanics involved. The authors previously developed a motion planning method for general graspless manipulation by robot fingertips, but it requires a great deal of computation. In this paper, we present an improved method based on a probabilistic technique, RRTs (Rapidly-exploring Random Trees), to accelerate planning. The method is applied to the planning of graspless manipulation where the manipulated object has three degrees of freedom, while our previous planner can deal with manipulation where the object has only one degree of freedom.

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