The Capability Map: A Tool to Analyze Robot Arm Workspaces

Zacharias, Franziska; Borst, Christoph; Wolf, Sebastian; Hirzinger, Gerd

doi:10.1142/s021984361350031x

Cited by 23 publications

(6 citation statements)

References 20 publications

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“…The Reachability map (RM), introduced by [6], describes how a robot can reach certain workspace poses by its end-effector. To achieve this, a large number of poses are sampled.…”

Section: A Reachability Mapmentioning

confidence: 99%

iDRM: Humanoid motion planning with realtime end-pose selection in complex environments

Yang

Ivan

et al. 2016

2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids)

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In this paper, we propose a novel inverse Dynamic Reachability Map (iDRM) that allows a floating base system to find valid end-poses in complex and dynamically changing environments in real-time. End-pose planning for valid stance pose and collision-free configuration is an essential problem for humanoid applications, such as providing goal states for walking and motion planners. However, this is non-trivial in complex environments, where standing locations and reaching postures are restricted by obstacles. Our proposed iDRM customizes the robot-to-workspace occupation list and uses an online update algorithm to enable efficient reconstruction of the reachability map to guarantee that the selected endposes are always collision-free. The iDRM was evaluated in a variety of reaching tasks using the 38 degree-of-freedom (DoF) humanoid robot Valkyrie. Our results show that the approach is capable of finding valid end-poses in a fraction of a second. Significantly, we also demonstrate that motion planning algorithms integrating our end-pose planning method are more efficient than those not utilizing this technique.

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“…The Reachability map (RM), introduced by [6], describes how a robot can reach certain workspace poses by its end-effector. To achieve this, a large number of poses are sampled.…”

Section: A Reachability Mapmentioning

confidence: 99%

iDRM: Humanoid motion planning with realtime end-pose selection in complex environments

Yang

Ivan

et al. 2016

2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids)

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“…An approach for directly quantifying manipulability of a redundant robot in task space is proposed by Zacharias et al [35]. They introduce a capability map, to guide the decision on how to place a mobile robot relative to an object.…”

Section: Optimizing Manipulabilitymentioning

confidence: 99%

Efficient Closed-Form Task Space Manipulability for a 7-DOF Serial Robot

Huber

Wollherr

2019

Robotics

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With the increasing demand for robots to react and adapt to unforeseen events, it is essential that a robot preserves agility at all times. While manipulability is a common measure to quantify agility at a given joint configuration, an efficient direct evaluation in task space is usually not possible with conventional methods, especially for redundant robots with an infinite number of Inverse Kinematic solutions. Yet, this is essential for global online optimization of a robot posture. In this work, we derive analytical expressions for a conventional 7-degrees of freedom (7-DOF) serial robot structure, which enable the direct evaluation of manipulability from a reduced task space parametrization. The resulting expressions allow array operation and thus achieve very high computational efficiency with vector-optimized programming languages. This direct and simultaneous calculation of the task space manipulability for large numbers of poses benefits many optimization problems in robotic applications. We show applications in global optimization of robot mounting poses, as well as redundancy resolution with global online optimization w.r.t. manipulability.

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“…Zacharias et al [2], [3] proposed a robot capa- bility/reachability map (RM) to analyze, record, and access the information about how a fixed-base manipulator can reach different workspace poses. The concept of reachability has also been applied in other domains, such as human-robot interaction [4] where the RM is used to guide the robot movement; and multi-contact locomotion [5], [6] where the reachability is used to automatically find possible contacts for legged systems.…”

Section: Introductionmentioning

confidence: 99%

Efficient Humanoid Motion Planning on Uneven Terrain Using Paired Forward-Inverse Dynamic Reachability Maps

Yang

Merkt

Ferrolho

et al. 2017

IEEE Robot. Autom. Lett.

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Abstract-A key prerequisite for planning manipulation with locomotion of humanoid robots in complex environments is to find a valid end-pose with a stable stance location and a collisionfree, balanced full-body configuration. Prior work based on the Inverse Reachability Map assumed that the feet are placed next to each other around the stance location on a horizontal plane. Additionally, the success rate was correlated with the coverage density of the sampled space, which is in turn limited by the memory needed for storing the map.We present a Paired Forward-Inverse Dynamic Reachability Maps that extends the inverse Dynamic Reachability Map (iDRM) by integrating it with forward reachability maps according to the inherent kinematic structure of the robot. By exploiting the combinatorics of this modularity, greater coverage in each map can be achieved while keeping a low number of stored samples. This enables us to draw samples from a much richer dataset to effectively plan end-poses for single-handed as well as bimanual tasks on uneven terrain. We demonstrated the method on the 38-DoF NASA Valkyrie humanoid utilizing the whole body to exploit redundancy for accomplishing manipulation tasks on uneven terrain while avoiding obstacles.

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The Capability Map: A Tool to Analyze Robot Arm Workspaces

Cited by 23 publications

References 20 publications

iDRM: Humanoid motion planning with realtime end-pose selection in complex environments

iDRM: Humanoid motion planning with realtime end-pose selection in complex environments

Efficient Closed-Form Task Space Manipulability for a 7-DOF Serial Robot

Efficient Humanoid Motion Planning on Uneven Terrain Using Paired Forward-Inverse Dynamic Reachability Maps

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