Simultaneous Grasp and Motion Planning

2010 IEEE International Conference on Robotics and Automation, 2010

In this work, we present an integrated planner for collision-free single and dual arm grasping motions. The proposed Grasp-RRT planner combines the three main tasks needed for grasping an object: finding a feasible grasp, solving the inverse kinematics and searching a collision-free trajectory that brings the hand to the grasping pose. Therefore, RRTbased algorithms are used to build a tree of reachable and collision-free configurations. During RRT-generation, potential grasping positions are generated and approach movements toward them are computed. The quality of reachable grasping poses is scored with an online grasp quality measurement module which is based on the computation of applied forces in order to diminish the net torque. We also present an extension to a dual arm planner which generates bimanual grasps together with corresponding dual arm grasping motions. The algorithms are evaluated with different setups in simulation and on the humanoid robot ARMAR-III.

2012

In this work, we present an integrated approach for planning collision-free grasping motions. The proposed Grasp-RRT planner combines the three main tasks needed for grasping an object: building a feasible grasp, solving the inverse kinematics problem and searching a collision-free trajectory that brings the hand to the grasping pose. Therefore, RRT-based algorithms are used to build a tree of reachable and collisionfree configurations. During RRT-generation, grasp hypotheses are generated and approach movements toward them are computed. The quality of reachable grasping poses is evaluated via grasp wrench space analysis. We also present an extension to a dual arm planner which generates bimanual grasps together with corresponding dual arm grasping motions. The algorithms are evaluated with different setups in simulation and on the humanoid robot ARMAR-III.

2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009

In this paper, we present efficient solutions for planning motions of dual-arm manipulation and re-grasping tasks. Motion planning for such tasks on humanoid robots with a high number of degrees of freedom (DoF) requires computationally efficient approaches to determine the robot's full joint configuration at a given grasping position, i.e. solving the Inverse Kinematics (IK) problem for one or both hands of the robot. In this context, we investigate solving the inverse kinematics problem and motion planning for dual-arm manipulation and re-grasping tasks by combining a gradient-descent approach in the robot's pre-computed reachability space with random sampling of free parameters. This strategy provides feasible IK solutions at a low computation cost without resorting to iterative methods which could be trapped by joint-limits. We apply this strategy to dual-arm motion planning tasks in which the robot is holding an object with one hand in order to generate whole-body robot configurations suitable for grasping the object with both hands. In addition, we present two probabilistically complete RRT-based motion planning algorithms (J + -RRT and IK-RRT) that interleave the search for an IK solution with the search for a collision-free trajectory and the extension of these planners to solving re-grasping problems. The capabilities of combining IK methods and planners are shown both in simulation and on the humanoid robot ARMAR-III performing dual-arm tasks in a kitchen environment.

2009 9th IEEE-RAS International Conference on Humanoid Robots, 2009

In this paper we present an approach for generating collision-free grasping motions and robustly execute them on a humanoid robot. The proposed MultiEEF-RRT algorithm for planning collision-free grasping trajectories exploits the enlarged goal space of a humanoid robot that results from the parallelized search of grasping trajectories for each arm. Here, multiple paths are searched simultaneously and the planner automatically chooses the first found solution. The reactive execution component operates on the planned C-Space trajectories and observes the movements in workspace with visual servoing approaches. The proposed algorithms do not rely on hand-eye calibrations, however it is possible to reliably execute given trajectories. The approach is fault-tolerant against changing execution speed, inaccurate sensor data and inexact executions of velocities. Since the hand and the target poses are visually tracked, the Cartesian error between the estimated position on a trajectory and the visually retrieved hand pose can be determined in workspace. This value is projected in the configuration space and used as a correction factor when calculating the joint velocities. We realized a grasping scenario with the humanoid robot ARMAR-III, where an object in front of the robot should be grasped. This demonstration shows how the proposed components play together to build a reactive and robust system integrating planning and execution of collisionfree motions.

Motion Planning for Humanoid Robots, 2010

The control system of a robot operating in a human-centered environments should address the problem of motion planning to generate collision-free motions for grasping and manipulation tasks. To deal with the complexity of these tasks in such environments, different motion planning algorithms can be used. We present a motion planning framework for manipulation and grasping tasks consisting of different components for sampling-based motion planning, collision checking, integrated motion planning and inverse kinematics as well as for planning of single arm grasping and dual-arm re-grasping tasks. We provide an evaluation of the presented methods on the humanoid robot ARMAR-III.

2010

This paper presents grasp planning for a multifingered hand with a humanoid robot. Our planner selects different ways of grasping even for the same object according to object position/orientation. If the planner cannot find a feasible grasp with arm/hand kinematics, it switches to full body motion planning. These functions are necessary for realizing the robust grasp planning. Our planner defines convex models on both the object and each grasp type. In considering geometrical relationships among these convex models, we determine the parameters required to define the final grasping configuration. We demonstrate effectiveness of grasp planning through simulation and experimental results.

Robotics and Autonomous Systems, 2012

This paper presents a simple grasp planning method for a multifingered hand. Its purpose is to compute a context-independent and dense set or list of grasps, instead of just a small set of grasps regarded as optimal with respect to a given criterion. By context-independent, we mean that only the robot hand and the object to grasp are considered. The environment and the position of the robot base with respect to the object are considered in a further stage. Such a dense set can be computed offline and then used to let the robot quickly choose a grasp adapted to a specific situation. This can be useful for manipulation planning of pick-and-place tasks. Another application is human-robot interaction when the human and robot have to hand over objects to each other. If human and robot have to work together with a predefined set of objects, grasp lists can be employed to allow a fast interaction. The proposed method uses a uniform sampling of the possible hand approaches. As this leads to many finger inverse kinematics tests, hierarchical data structures are employed to reduce the computation times. The data structures allow a fast determination of the points where the fingers can realize a contact with the object surface. The grasps are ranked according to a grasp quality criterion so that the robot will first parse the list from best to worse quality grasps, until it finds a grasp that is valid for a particular situation.

2009 IEEE International Conference on Robotics and Automation, 2009

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.

2008

We present a novel motion planning algorithm for performing constrained tasks such as opening doors and drawers by robots such as humanoid robots or mobile manipulators. Previous work on constrained manipulation transfers rigid constraints imposed by the target object motion directly into the robot configuration space. This often unnecessarily restricts the allowable robot motion, which can prevent the robot from performing even simple tasks, particularly if the robot has limited reachability or low number of joints. Our method computes "caging grasps" specific to the object and uses efficient search algorithms to produce motion plans that satisfy the task constraints. The major advantages of our technique significantly increase the range of possible motions of the robot by not having to enforce rigid constraints between the end-effector and the target object. We illustrate our approach with experimental results and examples running on two robot platforms.

Computer Graphics Forum, 2003

We present new techniques that use motion planning algorithms based on probabilistic roadmaps to control 22 degrees of freedom (DOFs) of human-like characters in interactive applications. Our main purpose is the automatic synthesis of collision-free reaching motions for both arms, with automatic column control and leg flexion. Generated motions are collision-free, in equilibrium, and respect articulation range limits. In order to deal with the high (22) dimension of our configuration space, we bias the random distribution of configurations to favor postures most useful for reaching and grasping. In addition, extensions are presented in order to interactively generate object manipulation sequences: a probabilistic inverse kinematics solver for proposing goal postures matching pre-designed grasps; dynamic update of roadmaps when obstacles change position; online planning of object location transfer; and an automatic stepping control to enlarge the character's reachable space. This is, to our knowledge, the first time probabilistic planning techniques are used to automatically generate collision-free reaching motions involving the entire body of a human-like character at interactive frame rates. Categories and Subject Descriptors (according to ACM CCS): I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism † Work done while at EPFL -Virtual Reality Lab nity. Most of the techniques developed 1 have not sufficiently explored this domain.