Analysis of the inertia and dynamics of grasped objects, for choosing optimal grasps to enable torque-efficient post-grasp manipulations

Mavrakis, Nikos; Ghalamzan, M E Amir; Baronti, Luca; Kopicki, Marek; Castellani, Marco

doi:10.1109/humanoids.2016.7803274

Cited by 24 publications

(20 citation statements)

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“…This paper extends our previous ideas on "task-informed grasp selection" [4], [5], by proposing a method for choosing grasps which maximise safety during post-grasp manipulations, in dynamic and uncertain environments where collisions are both likely and also safety-critical. Motivating applications include human-robot collaborative working, or remote manipulation in highly cluttered and high-consequence environments such as nuclear decommissioning and robotic surgery [6], [7].…”

Section: Introductionmentioning

confidence: 82%

“…• repeating the experiments with heavy-duty industrial manipulators, where higher payloads can be supported so that more accurate impact measurements can be acquired; • combining the post-grasp collision safety metric, proposed in this paper, with "graspability" or "grasp likelihood" metrics, obtained from state-of-the-art grasp planners, in order to mutually optimise grasp safety with grasp stability; • combining the ideas proposed in this paper with other methods for estimating the mass and mass distribution of an object; • combining the ideas of this paper with our previous works [5], [4] on task-relevant grasp planning, to provide a series of criteria for achieving grasp selection via multi-objective optimisation.…”

Section: Discussionmentioning

confidence: 99%

“…achieving the desired post-grasp motion. Our previous work showed how poor choices of grasp may make the desired movement of the grasped object kinematically infeasible for the arm to achieve [4], or can result in unnecessarily high joint torques during post-grasp manipulations [5].…”

Section: Introductionmentioning

confidence: 99%

“…The augmented object model describes the dynamics of a manipulator while holding an object. In our previous work [5], we used this augmented model to select grasps which minimised the post-grasp torque effort.…”

Section: Introductionmentioning

confidence: 99%

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Safe robotic grasping: Minimum impact-force grasp selection

Mavrakis

Ghalamzan

2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-This paper addresses the problem of selecting from a choice of possible grasps, so that impact forces will be minimised if a collision occurs while the robot is moving the grasped object along a post-grasp trajectory. Such considerations are important for safety in human-robot interaction, where even a certified "human-safe" (e.g. compliant) arm may become hazardous once it grasps and begins moving an object, which may have significant mass, sharp edges or other dangers. Additionally, minimising collision forces is critical to preserving longevity of robots which operate in uncertain and hazardous environments, e.g. robots deployed for nuclear decommissioning, where removing a damaged robot from a contaminated zone for repairs may be extremely difficult and costly. Also, unwanted collisions between a robot and critical infrastructure (e.g. pipework) in such high-consequence environments can be disastrous. In this paper we investigate how the safety of the post-grasp motion can be considered during the pre-grasp approach phase, so that the selected grasp is optimal in terms applying minimum impact forces if a collision occurs during a desired post-grasp manipulation. We build on the methods of augmented robot-object dynamics models and "effective mass" and propose a method for combining these concepts with modern grasp and trajectory planners, to enable the robot to achieve a grasp which maximises the safety of the postgrasp trajectory, by minimising potential collision forces. We demonstrate the effectiveness of our approach through several experiments with both simulated and real robots.

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Section: Introductionmentioning

confidence: 82%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Safe robotic grasping: Minimum impact-force grasp selection

Mavrakis

Ghalamzan

2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…In contrast, [12], [17] studied the optimal grasps resulting in a maximum manipulability at initial grasp configuration. Similarly, [15], [18] showed that different grasps can result in different task-oriented force manipulabilities as well as different torque efforts over the post-grasp motions. However, the main assumption of these works is that a planner can generate many stable grasp poses.…”

Section: Related Workmentioning

confidence: 96%

Human-in-the-loop optimisation: Mixed initiative grasping for optimally facilitating post-grasp manipulative actions

Ghalamzan¹,

Abi-Farraj²,

Giordano³

2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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This paper addresses the problem of mixed initiative, shared control for master-slave grasping and manipulation. We propose a novel system, in which an autonomous agent assists a human in teleoperating a remote slave arm/gripper, using a haptic master device. Our system is designed to exploit the human operator's expertise in selecting stable grasps (still an open research topic in autonomous robotics). Meanwhile, a-priori knowledge of: i) the slave robot kinematics, and ii) the desired post-grasp manipulative trajectory, are fed to an autonomous agent which transmits force cues to the human, to encourage maximally manipulable grasp pose selections. Specifically, the autonomous agent provides force cues to the human, during the reach-to-grasp phase, which encourage the human to select grasp poses which maximise manipulation capability during the post-grasp object manipulation phase. We introduce a task-oriented velocity manipulability cost function (TOV), which is used to identify the maximum kinematic capability of a manipulator during post-grasp motions, and feed this back as force cues to the human during the pre-grasp phase. We show that grasps which minimise TOV result in significantly reduced control effort of the manipulator, compared to other feasible grasps. We demonstrate the effectiveness of our approach by experiments with both real and simulated robots.

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