Graspability: A description of work surfaces for planning of robot manipulation sequences

Ruehl, Steffen W.; Hermann, A. M.; Xue, Zhixing; Kerscher, Thilo; Dillmann, Ruediger

doi:10.1109/icra.2011.5979779

Cited by 10 publications

(2 citation statements)

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“…Similar to the door task, the operator first teleoperated CHIMP to a favorable position near the valve, as determined by a priori guides visualized on the operator interface. These markers showed regions of maximum reachability, computed using a discrete cost‐map of kinematic reachability, similar to Ruehl, Hermann, Xue, Kerscher, & Dillmann (). Navigating to favorable base locations such as this greatly reduced the time required to complete the task, as kinematic reachability greatly influenced the robot's ability to fully turn a valve.…”

Section: Software Preparation For the Drc Finalssupporting

confidence: 62%

Developing a Robust Disaster Response Robot: CHIMP and the Robotics Challenge

Haynes

Stager

Stentz

et al. 2017

Journal of Field Robotics

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CHIMP, the CMU Highly Intelligent Mobile Platform, is a humanoid robot capable of executing complex tasks in dangerous, degraded, human‐engineered environments, such as those found in disaster response scenarios. CHIMP is uniquely designed for mobile manipulation in challenging environments, as the robot performs manipulation tasks using an upright posture, yet it uses more stable prostrate postures for mobility through difficult terrain. In this paper, we report on the improvements made to CHIMP—both in its mechanical design and its software systems—in preparation for the DARPA Robotics Challenge Finals in June 2015. These include details on CHIMP's novel mechanical design, actuation systems, robust construction, all‐terrain mobility, supervised autonomy approach, and unique user interfaces utilized for the challenge. Additionally, we provide an overview of CHIMP's performance, and we detail the various lessons learned over the course of the challenge. CHIMP was one of the winners of the DARPA Robotics Challenge, completing all tasks and finishing in 3rd place out of 23 teams. Notably, CHIMP was the only robot to stand back up after accidentally falling over, a testament to the robustness engineered into the robot and a remote operator's ability to execute complex tasks using a highly capable robot. We present CHIMP as a concrete engineering example of a successful disaster response robot.

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Section: Software Preparation For the Drc Finalssupporting

confidence: 62%

Developing a Robust Disaster Response Robot: CHIMP and the Robotics Challenge

Haynes

Stager

Stentz

et al. 2017

Journal of Field Robotics

View full text Add to dashboard Cite

show abstract

“…For example, [23] sampled a robotic arm model in joint space and constructed a capability map to describe the spatial reachability of the arm. Spatial regions with high reachability index values are prioritized for task planning, such as generating manipulation sequences [24]. The capability map can be constructed offline and easily adapt to new tasks and objects.…”

Section: A Robot Grasp Synthesismentioning

confidence: 99%

Exploiting Kinematic Redundancy for Robotic Grasping of Multiple Objects

Yao

Billard

2023

IEEE Trans. Robot.

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Humans coordinate the abundant degrees of freedom (DoFs) of hands to dexterously perform tasks in everyday life. We imitate human strategies to advance the dexterity of multi-DoF robotic hands. Specifically, we enable a robot hand to grasp multiple objects by exploiting its kinematic redundancy, referring to all its controllable DoFs. We propose a humanlike grasp synthesis algorithm to generate grasps using pairwise contacts on arbitrary opposing hand surface regions, no longer limited to fingertips or hand inner surface. To model the available space of the hand for grasp, we construct a reachability map, consisting of reachable spaces of all finger phalanges and the palm. It guides the formulation of a constrained optimization problem, solving for feasible and stable grasps. We formulate an iterative process to empower robotic hands to grasp multiple objects in sequence. Moreover, we propose a kinematic efficiency metric and an associated strategy to facilitate exploiting kinematic redundancy. We validated our approaches by generating grasps of single and multiple objects using various hand surface regions. Such grasps can be successfully replicated on a real robotic hand.

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