Planning an Efficient and Robust Base Sequence for a Mobile Manipulator Performing Multiple Pick-and-place Tasks

Xu, Jingren; Harada, Kensuke; Wan, Weiwei; Ueshiba, Toshio; Domae, Yukiyasu

doi:10.1109/icra40945.2020.9196999

Cited by 23 publications

(8 citation statements)

References 19 publications

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“…Different base regions are colored differently. Since some of the qualitative results of the base region have been presented in [14], here we focus on the quantitative of the planner.…”

Section: Numerical Results and Analysismentioning

confidence: 99%

“…Instead, we employ the decoupled approach and put our effort on reducing the number of base movements. This paper describes a planner that generalizes our previous conference contributions [14]. The conference version presented a special case of the current version, i.e., planning a globally static base sequence for the mobile manipulator to collect all the objects in a tray.…”

Section: B Compared To Existing Researchmentioning

confidence: 99%

“…In order to determine the feasibility of a grasping pose, firstly we solve inverse kinematics (IK) and then check if the IK solutions are collision-free. We use the method presented in our previous work [14] to solve inverse kinematics If there exists at least one grasping pose G ti ∈ {G t1 , G t2 , . .…”

Section: B Base Region Calculationmentioning

confidence: 99%

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Planning a Minimum Sequence of Positions for Picking Parts From Multiple Trays Using a Mobile Manipulator

Xu¹,

Domae

Ueshiba

et al. 2021

IEEE Access

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Mobile manipulators are able to operate in a large workspace, and have the potential to replace human workers to perform a sequence of pick-and-place tasks at separate locations. Many existing works optimize the base position or manipulator configuration for a single manipulation task, however, very few of them consider a sequence of tasks. In this paper, we present a planner that plans a minimum sequence of base positions for a mobile manipulator to robustly collect objects stored in multiple trays. We use inverse kinematics to determine the base region where a mobile manipulator can grasp the target objects stored in a tray, and move the mobile manipulator to the intersections of base regions to reduce the operation time for moving the base. We ensure robustness by only considering the intersection whose radius of the inscribed circle is larger than the base positioning error. Then the minimization of the number of base positions is formulated as a 0-1 knapsack problem. Besides, considering different object placements in the tray, we analyze feasible policies for dynamically updating the base sequence based on either the remaining objects or the target objects to be picked. In the experiment, we examine our planner on various scenarios, including different object placements: (1) Regularly placed toy objects; (2) Randomly placed industrial parts; and different implementation policies: (1) Apply globally static base positions; (2) Dynamically update the base positions. The experiment results show that the time for moving the base decreases by 11.22 seconds (29.37%) to 17.26 seconds (36.77%) by reducing one base movement, and demonstrate the feasibility and potential of the proposed method.INDEX TERMS Mobile manipulation, manufacturing automation, part-supply tasks, pick-and-place, tasklevel planning.

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“…Different base regions are colored differently. Since some of the qualitative results of the base region have been presented in [14], here we focus on the quantitative of the planner.…”

Section: Numerical Results and Analysismentioning

confidence: 99%

Section: B Compared To Existing Researchmentioning

confidence: 99%

See 1 more Smart Citation

Planning a Minimum Sequence of Positions for Picking Parts From Multiple Trays Using a Mobile Manipulator

Xu¹,

Domae

Ueshiba

et al. 2021

IEEE Access

Self Cite

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show abstract

“…Note that a part of this paper has been presented in [32], it presented a special case of the current version, which planned a globally static base sequence for the mobile manipulator to collect all the objects in a tray. Different from the published conference paper, we in this journal version provide extended discussions on the implication of the result on designing tray configurations in the storage area.…”

Section: Related Workmentioning

confidence: 99%

Planning a Sequence of Base Positions for a Mobile Manipulator to Perform Multiple Pick-and-Place Tasks

Xu¹,

Domae²,

Ueshiba³

et al. 2020

Preprint

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In this paper, we present a planner that plans a sequence of base positions for a mobile manipulator to efficiently and robustly collect objects stored in distinct trays. We achieve high efficiency by exploring the common areas where a mobile manipulator can grasp objects stored in multiple trays simultaneously and move the mobile manipulator to the common areas to reduce the time needed for moving the mobile base. We ensure robustness by optimizing the base position with the best clearance to positioning uncertainty so that a mobile manipulator can complete the task even if there is a certain deviation from the planned base positions. Besides, considering different styles of object placement in the tray, we analyze feasible schemes for dynamically updating the base positions based on either the remaining objects or the target objects to be picked in one round of the tasks. In the experiment part, we examine our planner on various scenarios, including different object placement: (1) Regularly placed toy objects; (2) Randomly placed industrial parts; and different schemes for online execution: (1) Apply globally static base positions; (2) Dynamically update the base positions. The experiment results demonstrate the efficiency, robustness and feasibility of the proposed method.Note to Practitioners-The presented project uses mobile manipulators to fetch and supply parts in an automotive assembly factory. Mechanical parts at these sites are usually regularly or randomly placed in supply trays. The project develops methods to explore robust mobile base positions where the objects with different placement styles and from different trays can be reached by a mobile manipulator. The mobile manipulators could perform efficient and high-quality pick-and-place operations by navigating to the explored positions. The developed methods also discuss the dynamic updates the base positions following picking process to further increase system robustness. The presented project is expected to shed light on the deployment of mobile manipulators to collect parts at large manufacturing factories.

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“…Although currently used robots mostly manipulate objects by picking them up once [1]- [3], a pick-and-place manipulation is energy consuming and is not adequate for manipulating a large and heavy object, since the grasped object has to be completely lifted. On the other hand, a human can effectively select an adequate manipulation strategy, taking into account both the features of the task and the physical parameters of the object.…”

Section: Introductionmentioning

confidence: 99%

Controlling Pivoting Gait Using Graph Model Predictive Control

et al. 2021

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Pivoting gait, in which a robot iteratively tilts an object, rotates it around a vertex, and then places it down on the floor, is efficient for manipulating a large and heavy object with a relatively small manipulating force. However, pivoting gait can easily fail, even with a small external disturbance, due to its instability. To address this problem, we propose a controller to robustly control the object's motion during pivoting gait by introducing two gait modes, i.e., double-support mode, which can manipulate a relatively light object with higher speed, and quadruple-support mode, which can manipulate a relatively heavy object with slower speed. To control the pivoting gait, a graph model predictive control is applied by considering these two gait modes. The experiments show that by adaptively switching the gait mode according to the applied external disturbance, a robot can stably perform the pivoting gait even when an external disturbance is applied to the object. The experimental results lead us to automate the manipulation of a large and heavy object.INDEX TERMS Feedback control, graph search, model predictive control, pivoting manipulation, nonprehensile manipulation.

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Planning an Efficient and Robust Base Sequence for a Mobile Manipulator Performing Multiple Pick-and-place Tasks

Cited by 23 publications

References 19 publications

Planning a Minimum Sequence of Positions for Picking Parts From Multiple Trays Using a Mobile Manipulator

Planning a Minimum Sequence of Positions for Picking Parts From Multiple Trays Using a Mobile Manipulator

Planning a Sequence of Base Positions for a Mobile Manipulator to Perform Multiple Pick-and-Place Tasks

Controlling Pivoting Gait Using Graph Model Predictive Control

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