Dual-Arm Coordinated Motion Planning and Compliance Control for Capturing Moving Objects with Large Momentum

Lei, Yan; Yang, Yiming; Xu, Wenfu; Vijayakumar, Sethu

doi:10.1109/iros.2018.8593853

Cited by 18 publications

(12 citation statements)

References 23 publications

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“…The force capability optimization is achieved in the null space of manipulation task by using the proposed task-oriented manipulability optimization (12).…”

Section: B Null-space Manipulability Optimizationmentioning

confidence: 99%

“…limited to 10 N, while the constant δ (see Appendix A) are set to 1, 2, and 3 N, respectively. For the torque-controlled robots, the feedback gains K p , K v in (23) are set to diag( [16,16,16,12,12,12,12]) and diag([0.2, 0.2, 0.2, 0.1, 0.1, 0.1, 0.1]), respectively. The interaction between the object and the robots is modelled with a spring-damper (stiffness 5000 N/m and damping 500 Ns/m).…”

Section: Da 3 C For Multi-robot Collaborationmentioning

confidence: 99%

“…Redundancy exploitation: For dexterous manipulation [11], [12], redundant manipulators were adopted to enlarge workspace, avoid singularities and collisions with the environment. Further, manipulator's redundancy can also be explored to maximize manipulability [13].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Decentralized Ability-Aware Adaptive Control for Multi-Robot Collaborative Manipulation

Yan

Stouraitis

Vijayakumar

2021

IEEE Robot. Autom. Lett.

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Multi-robot teams can achieve more dexterous, complex and heavier payload tasks than a single robot, yet effective collaboration is required. Multi-robot collaboration is extremely challenging due to the different kinematic and dynamics capabilities of the robots, the limited communication between them, and the uncertainty of the system parameters. In this letter, a Decentralized Ability-Aware Adaptive Control (DA 3 C ) is proposed to address these challenges based on two key features. Firstly, the common manipulation task is represented by the proposed nominal task ellipsoid, which is used to maximize each robot's force capability online via optimizing its configuration. Secondly, a decentralized adaptive controller is designed to be Lyapunov stable in spite of heterogeneous actuation constraints of the robots and uncertain physical parameters of the object and environment. In the proposed framework, decentralized coordination and load distribution between the robots is achieved without communication, while only the control deficiency is broadcast if any of the robots reaches its force limits. In this case, the object's reference trajectory is modified in a decentralized manner to guarantee stable interaction. Finally, we perform several numerical and physical simulations to analyse and verify the proposed method with heterogeneous multi-robot teams in collaborative manipulation tasks.

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“…The force capability optimization is achieved in the null space of manipulation task by using the proposed task-oriented manipulability optimization (12).…”

Section: B Null-space Manipulability Optimizationmentioning

confidence: 99%

Section: Da 3 C For Multi-robot Collaborationmentioning

confidence: 99%

See 1 more Smart Citation

Decentralized Ability-Aware Adaptive Control for Multi-Robot Collaborative Manipulation

Yan

Stouraitis

Vijayakumar

2021

IEEE Robot. Autom. Lett.

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show abstract

“…In this paper, the weighted force manipulability ellipsoid (WFME) will be adopted to optimize the force polytope of the manipulator. Redundancy exploitation: For dexterous manipulation [11], [12], redundant manipulators were adopted to enlarge workspace, avoid singularities and collisions with the environment. Further, manipulator's redundancy can also be explored to maximize manipulability [13].…”

Section: Introductionmentioning

confidence: 99%

Decentralized Ability-Aware Adaptive Control for Multi-robot Collaborative Manipulation

Lei

Stouraitis

Vijayakumar

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Multi-robot teams can achieve more dexterous, complex and heavier payload tasks than a single robot, yet effective collaboration is required. Multi-robot collaboration is extremely challenging due to the different kinematic and dynamics capabilities of the robots, the limited communication between them, and the uncertainty of the system parameters. In this paper, a Decentralized Ability-Aware Adaptive Control (DA 3 C) is proposed to address these challenges based on two key features. Firstly, the common manipulation task is represented by the proposed nominal task ellipsoid, which is used to maximize each robot's force capability online via optimizing its configuration. Secondly, a decentralized adaptive controller is designed to be Lyapunov stable in spite of heterogeneous actuation constraints of the robots and uncertain physical parameters of the object and environment. In the proposed framework, decentralized coordination and load distribution between the robots is achieved without communication, while only the control deficiency is broadcast if any of the robots reaches its force limits. In this case, the object's reference trajectory is modified in a decentralized manner to guarantee stable interaction. Finally, we perform several numerical and physical simulations to analyse and verify the proposed method with heterogeneous multi-robot teams in collaborative manipulation tasks.

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“…Similar tracking combined with interception strategy can be found in ref. [27], where a dual-arm coordinated motion are realized by optimization and compliance control method without disturbing the tracking motion. Marturi et al [5] built a switching planning method, in which a global planner is used to quickly reach the new grasp area, while the local planner is used to implement the smooth tracking thus keeping the gripper in the pre-grasp pose.…”

Section: Introductionmentioning

confidence: 99%

Tracking and grasping of moving target based on accelerated geometric particle filter on colored image

Gong

Qiu

Tao

et al. 2020

Sci. China Technol. Sci.

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Visual tracking and grasping of moving object is a challenging task in the field of robotic manipulation, which also has great potential in applications such as human-robot collaboration. Based on the particle filtering framework and position-based visual servoing, this paper proposes a new method for visual tracking and grasping of randomly moving objects. A geometric particle filter tracker is established for visual tracking. In order to deal with the tracking efficiency issue for particle filter, edge detection and morphological dilation are employed to reduce the computation burden of geometric particle filtering. Meanwhile, the HSV image feature is employed instead of the grayscale feature to improve the tracking algorithm's robustness to illumination change. A grasping strategy combining tracking and interception is adopted along with the position-based visual servoing (PBVS) method to achieve stable grasp of the target. Comprehensive comparisons on open source dataset and a large number of experiments on real robot system are conducted, which demonstrate the proposed method has competitive performance in random moving object tracking and grasping.

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Dual-Arm Coordinated Motion Planning and Compliance Control for Capturing Moving Objects with Large Momentum

Cited by 18 publications

References 23 publications

Decentralized Ability-Aware Adaptive Control for Multi-Robot Collaborative Manipulation

Decentralized Ability-Aware Adaptive Control for Multi-Robot Collaborative Manipulation

Decentralized Ability-Aware Adaptive Control for Multi-robot Collaborative Manipulation

Tracking and grasping of moving target based on accelerated geometric particle filter on colored image

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