A manipulation motion planner for dual-arm industrial manipulators

Harada, Kensuke; Tsuji, Tokuo; Laumond, Jean–Paul

doi:10.1109/icra.2014.6906965

Cited by 42 publications

(28 citation statements)

References 19 publications

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“…Previous work considering regraspings used such moves mainly for the purpose of changing grasps, either one robot changing from one grasp to another or changing from one robot grasping to another robot grasping [13], [14]. In this work, we utilize regrasping moves not necessarily to change grasps.…”

Section: B Regraspingmentioning

confidence: 99%

Closed-Chain Manipulation of Large Objects by Multi-Arm Robotic Systems

Xian

Lertkultanon

Pham

2017

IEEE Robot. Autom. Lett.

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Abstract-Closed kinematic chains are created whenever multiple robot arms concurrently manipulate a single object. The closed-chain constraint, when coupled with robot joint limits, dramatically changes the connectivity of the configuration space. We propose a regrasping move, termed "IK-switch", which allows efficiently bridging components of the configuration space that are otherwise mutually disconnected. This move, combined with several other developments, such as a method to stabilize the manipulated object using the environment, a new tree structure, and a compliant control scheme, enables us to address complex closed-chain manipulation tasks, such as flipping a chair frame, which is otherwise impossible to realize using existing multi-arm planning methods.

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Section: B Regraspingmentioning

confidence: 99%

Closed-Chain Manipulation of Large Objects by Multi-Arm Robotic Systems

Xian

Lertkultanon

Pham

2017

IEEE Robot. Autom. Lett.

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“…This involves several early work like [22]- [25], followed by more general implementations like [26]- [28]. More recent work like [29], [30] takes regrasp planning as a muti-modal mixture of grasps and motions and performs multi-modal planning. Comparing with our approach in this paper, these related work take a manipulation task as one big problem and try to solve the problem directly.…”

Section: ) Manipulation Planningmentioning

confidence: 98%

“…Experimental Settings 1) Simulation Environment: We use simulation to collect data and analyze the performance. Our simulation environment is the same as reference [30] where the object is a brick, the robot is the Kawada Nextage, the place for intermediate placement is a position on the table, and the obstaces are a bunch of caging bars. The settings were illustrated in Fig.2.…”

Section: Experiments and Analysismentioning

confidence: 99%

Divide-and-conquer manipulation planning by lazily searching a weighted two-layer manipulation graph

Wan

Harada

2015

2015 IEEE International Workshop on Advanced Robotics and Its Social Impacts (ARSO)

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When people fail to move his or her arms from one configuration to another, they attempt to break the task into smaller tasks and finish them separately. This kind of solution is usually named "divide and conquer". In this paper, we propose an implementation of "divide and conquer" where the robot attempts to divide one difficult manipulation task into smaller but easier problems according to the results of lazy planning. It leverages the planning of different levels to build a weighted two-layer manipulation graph, and divides and conquer the original task by lazily searching the weighted two-layer manipulation graph. In the lowest level, the planning is motion planning. In the middle level, the planning is grasp planning and placement planning. In the highest level, the planning is manipulation planning. Our implementation uses the grasps and placements computed in the middle level to construct a weighted two-layer manipulation graph for the highest level. It finds a manipulation path through the weighted two-layer manipulation graph in the highest level using lazy searching, and uses motion planning in the lowest level to find the motions that connect the vertices of the weighted two-layer manipulation path. Simulation is developed to demonstrate the the performance of our implementation. The manipulation task in the simulation is divided and separately conquered by leveraging the planning at different levels.

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“…Each circle in the graph represents one placement of the object, and each node on the circle represents one grasp. The edges encode the relationship between the grasps and the placements: One can be changed into another by the robot using transfer or transit motions [21]. The last step is to search the regrasp graph and do motion planning.…”

Section: A Single-arm Regraspmentioning

confidence: 99%

Developing and Comparing Single-Arm and Dual-Arm Regrasp

Wan

Harada

2016

IEEE Robot. Autom. Lett.

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Abstract-The goal of this paper is to develop efficient regrasp algorithms for single-arm and dual-arm regrasp and compares the performance of single-arm and dual-arm regrasp by running the two algorithms thousands of times. We focus on pick-and-place regrasp which reorients an object from one placement to another by using a sequence of pick-ups and placedowns. After analyzing the simulation results, we find dual-arm regrasp is not necessarily better than single-arm regrasp: Dualarm regrasp is flexible. When the two hands can grasp the object with good clearance, dual-arm regrasp is better and has higher successful rate than single-arm regrasp. However, dualarm regrasp suffers from geometric constraints caused by the two arms. When the grasps overlap, dual-arm regrasp is bad. Developers need to sample grasps with high density to reduce overlapping. This leads to exploded combinatorics in previous methods, but is possible with the algorithms presented in this paper. Following the results, practitioners may choose singlearm or dual-arm robots by considering the object shapes and grasps. Meanwhile, they can reduce overlapping and implement practical dual-arm regrasp by using the presented algorithms.

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A manipulation motion planner for dual-arm industrial manipulators

Cited by 42 publications

References 19 publications

Closed-Chain Manipulation of Large Objects by Multi-Arm Robotic Systems

Closed-Chain Manipulation of Large Objects by Multi-Arm Robotic Systems

Divide-and-conquer manipulation planning by lazily searching a weighted two-layer manipulation graph

Developing and Comparing Single-Arm and Dual-Arm Regrasp

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