Motion and Force Planning for Manipulating Heavy Objects by Pivoting

Fakhari, Amin; Patankar, Aditya; Chakraborty, Nilanjan

doi:10.1109/iros51168.2021.9636103

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…Although the above studies exercised contact state transitions and their applications in robotics, they assumed that the objects were lighter than the maximum payload of robots, which led to limitations in task flexibility and performance. To overcome the limitations, Fakhari et al [18] developed pivoting planners that took advantage of contact state transitions and support from contact points to manipulate heavy objects. Fan et al [19] studied the constraints of mobile manipulators and related non-prehensile manipulation planning while taking into account contact transitions.…”

Section: A Manipulation Considering Contact State Transitionsmentioning

confidence: 99%

A Dual-Arm Robot that Manipulates Heavy Plates Cooperatively with a Vacuum Lifter

Hayakawa¹,

Wan²,

Koyama³

et al. 2022

Preprint

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Section: A Manipulation Considering Contact State Transitionsmentioning

confidence: 99%

A Dual-Arm Robot that Manipulates Heavy Plates Cooperatively with a Vacuum Lifter

Hayakawa¹,

Wan²,

Koyama³

et al. 2022

Preprint

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“…The methods took advantage of environmental support to share partial object weight. The robot manipulator performs nonprehensile actions to push, flip or pivot large and heavy objects (Fakhari et al , 2021; Zhang et al , 2021; Hayakawa et al , 2021; Fan et al , 2019). Using collaborative manipulators while considering environmental support allowed the manipulation of large and heavy objects with affordable hardware costs.…”

Section: Introductionmentioning

confidence: 99%

Implicit contact-rich manipulation planning for a manipulator with insufficient payload

Kento

Wan

Harada

2023

RIA

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Purpose This paper aims to study using a mobile manipulator with a collaborative robotic arm component to manipulate objects beyond the robot’s maximum payload. Design/methodology/approach This paper proposes a single-short probabilistic roadmap-based method to plan and optimize manipulation motion with environment support. The method uses an expanded object mesh model to examine contact and randomly explores object motion while keeping contact and securing affordable grasping force. It generates robotic motion trajectories after obtaining object motion using an optimization-based algorithm. With the proposed method’s help, the authors plan contact-rich manipulation without particularly analyzing an object’s contact modes and their transitions. The planner and optimizer determine them automatically. Findings The authors conducted experiments and analyses using simulations and real-world executions to examine the method’s performance. The method successfully found manipulation motion that met contact, force and kinematic constraints. It allowed a mobile manipulator to move heavy objects while leveraging supporting forces from environmental obstacles. Originality/value This paper presents an automatic approach for solving contact-rich heavy object manipulation problems. Unlike previous methods, the new approach does not need to explicitly analyze contact states and build contact transition graphs, thus providing a new view for robotic grasp-less manipulation, nonprehensile manipulation, manipulation with contact, etc.

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