Manipulation planning under changing external forces

Chen, Lipeng; Figueredo, Luis Felipe da Cruz; Dogar, Mehmet R.

doi:10.1007/s10514-020-09930-z

Cited by 14 publications

(11 citation statements)

References 55 publications

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“…This highlights that it is possible to plan for multiple operations with human comfort in mind, ensuring no operation demands too much muscular activity. This result paves the way for integrating more elaborate motion/task planners that consider continuous and/or sequential forceful interactions (e.g., [15,17]) while minimizing the total muscular activity required from the human co-worker.…”

Section: Optimization Performance: Multiple Tasksmentioning

confidence: 92%

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Planning to Minimize the Human Muscular Effort during Forceful Human-Robot Collaboration

FigueredoLuis

Castro

ChenLipeng

et al. 2021

J. Hum.-Robot Interact.

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This work addresses the problem of planning a robot configuration and grasp to position a shared object during forceful human-robot collaboration, such as a puncturing or a cutting task. Particularly, our goal is to find a robot configuration that positions the jointly manipulated object such that the muscular effort of the human, operating on the same object, is minimized while also ensuring the stability of the interaction for the robot. This raises three challenges. First, we predict the human muscular effort given a human-robot combined kinematic configuration and the interaction forces of a task. To do this, we perform task-space to muscle-space mapping for two different musculoskeletal models of the human arm. Second, we predict the human body kinematic configuration given a robot configuration and the resulting object pose in the workspace. To do this, we assume that the human prefers the body configuration that minimizes the muscular effort. And third, we ensure that, under the forces applied by the human, the robot grasp on the object is stable and the robot joint torques are within limits. Addressing these three challenges, we build a planner that, given a forceful task description, can output the robot grasp on an object and the robot configuration to position the shared object in space. We quantitatively analyze the performance of the planner and the validity of our assumptions. We conduct experiments with human subjects to measure their kinematic configurations, muscular activity, and force output during collaborative puncturing and cutting tasks. The results illustrate the effectiveness of our planner in reducing the human muscular load. For instance, for the puncturing task, our planner is able to reduce muscular load by 69.5\% compared to a user-based selection of object poses.

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Section: Optimization Performance: Multiple Tasksmentioning

confidence: 92%

“…We explain the details of how we model the stability of the robot grasp and robot joints in Section 6. To do this, we mostly rely on an existing formulation of ours from previous work [15] but integrate it here with human-related constraints.…”

Section: Planning To Minimize Thementioning

confidence: 99%

Planning to Minimize the Human Muscular Effort during Forceful Human-Robot Collaboration

FigueredoLuis

Castro

ChenLipeng

et al. 2021

J. Hum.-Robot Interact.

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“…For example, Luo et al [10] developed algorithms to predict human reaching and proposed a planner to plan collaborative robot motion while avoiding collision with humans. Chen et al [11] developed a manipulation algorithm for a robot to hold an object for a human while resisting large external force from a human. Maeda et al [12] developed an algorithm to predict the intention of humans and use that to plan robot trajectory and coordinate the human-robot collaboration accordingly.…”

Section: A Human-robot Collaborationmentioning

confidence: 99%

Human-in-the-loop Robotic Manipulation Planning for Collaborative Assembly

Raessa

Chen

Wan

et al. 2019

Preprint

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This paper develops a robotic manipulation planner for human-robot collaborative assembly. Unlike previous methods which study an independent and fully AI-equipped autonomous system, this paper explores the subtask distribution between a robot and a human and studies a human-in-the-loop robotic system for collaborative assembly. The system distributes the subtasks of an assembly to robots and humans by exploiting their advantages and avoiding their disadvantages. The robot in the system will work on pick-and-place tasks and provide workpieces to humans. The human collaborator will work on fine operations like aligning, fixing, screwing, etc. A constraintbased incremental manipulation planning method is proposed to generate the motion for the robots. The performance of the proposed system is demonstrated by asking a human and the dual-arm robot to collaboratively assemble a cabinet. The results showed that the proposed system and planner are effective, efficient, and can assist humans in finishing the assembly task comfortably.Note to Practitioners-This paper was motivated by assembling a cabinet. The assembling process involves several pick-and-place, reorientation, regrasp, alignment, peg-in-hole, and screwing subtasks. The system distributes these subtasks to robots and humans by exploiting their advantages and avoiding their disadvantages. The robot used is a dual-arm robots with two parallel grippers. Thus, a suction cup tool is used for the pick-and-place of thin objects. Soft-finger contact constraints are considered to assure safe manipulation. Human ergonomics are included as a quality for optimizing the handover between robot arms and robot and human. The proposed planner and system show satisfying user experience and expedite the cabinet assembly. It is expected to help relax the labor-intensive manufacturing process in the future.

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“…Crucially, in our situation, planning regrasp trajectories to adjust the grasp points on the object and improve balance [10]- [12] is not an option, because releasing the grasp robot motion planning vision in-hand paths joint trajectories robot estimator DMG planning Fig. 1: A schematic overview of our proposed system.…”

Section: Introductionmentioning

confidence: 99%

Discrete Bimanual Manipulation for Wrench Balancing

Cruciani

Almeida

Kragić

et al. 2020

2020 IEEE International Conference on Robotics and Automation (ICRA)

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Dual-arm robots can overcome grasping force and payload limitations of a single arm by jointly grasping an object. However, if the distribution of mass of the grasped object is not even, each arm will experience different wrenches that can exceed its payload limits. In this work, we consider the problem of balancing the wrenches experienced by a dual-arm robot grasping a rigid tray. The distribution of wrenches among the robot arms changes due to objects being placed on the tray. We present an approach to reduce the wrench imbalance among arms through discrete bimanual manipulation. Our approach is based on sequential sliding motions of the grasp points on the surface of the object, to attain a more balanced configuration. We validate our modeling approach and system design through a set of robot experiments.

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Manipulation planning under changing external forces

Abstract: This is a repository copy of Manipulation planning under changing external forces.

Cited by 14 publications

References 55 publications

Planning to Minimize the Human Muscular Effort during Forceful Human-Robot Collaboration

Planning to Minimize the Human Muscular Effort during Forceful Human-Robot Collaboration

Human-in-the-loop Robotic Manipulation Planning for Collaborative Assembly

Discrete Bimanual Manipulation for Wrench Balancing

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