Real-Time Grasping Planning for Robotic Bin-Picking and Kitting Applications

Shi, Jane; Koonjul, Gurdayal S.

doi:10.1109/tase.2017.2671434

Cited by 28 publications

(9 citation statements)

References 30 publications

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“…The evaluation of the probability of the force closure increases the time to identify the grasping poses in [23], where the computation time spans from 72 to 284 s. Interesting results are reached by Graspit! [25] in which a constrained environment provides grasping poses after 10-15 s. A real-time computation of the grasp in a constrained arm is provided in [40], which also takes into account the kinematics of the arm but the quality of the grasp is qualitative.…”

Section: Discussionmentioning

confidence: 99%

“…If a complete environmental knowledge is available, an accurate simulation tool can be developed using multi-body dynamics tools in order to avoid collision [26], for example with a soft humanoid hand. This approach can be time-consuming; however, decomposing the problem in multiple low dimensional spaces can improve performance [40]. In some applications, one important constraint that needs to be considered relates to avoiding collisions with objects in the scene.…”

Section: Grasping In Constrained Environmentsmentioning

confidence: 99%

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Optimal Grasping Pose Synthesis in a Constrained Environment

et al. 2020

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In the last few decades, several approaches have been presented to accomplish tasks with robots or autonomous systems in a glovebox; nevertheless, in nuclear facilities, risky operations are still executed by humans that guarantee a high manipulation capability and dexterity. Inside the gloveboxes, robotic devices have to operate in cluttered environments, or environments with limited space for movement; therefore, it is of significant interest to identify grasping poses that are feasible within such constrained environments. In this paper, we present and experimentally evaluate a strategy to synthesise optimal grasps considering geometric primitives for a manipulation systems in a constrained environment. The novel strategy has been experimentally evaluated in a cluttered environment (as a glovebox mock-up) with realistic objects, and the efficacy of the proposed grasping algorithm is proposed.

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Section: Discussionmentioning

confidence: 99%

Section: Grasping In Constrained Environmentsmentioning

confidence: 99%

Optimal Grasping Pose Synthesis in a Constrained Environment

et al. 2020

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“…Thus, the real challenge in robots is when the robot gripper needs to plan and navigate in extremely cluttered environments [304]. In contrast to merely storing, kitting needs to prepare products or tools quickly, pick up objects from a cluttered place and place these objects down in cluttered environments by using real-time planning, which can overcome object complexity and detect collision issues [305].…”

Section: A Humanoid Robotsmentioning

confidence: 99%

Review of Deep Reinforcement Learning-Based Object Grasping: Techniques, Open Challenges, and Recommendations

2020

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The motivation behind our work is to review and analyze the most relevant studies on deep reinforcement learning-based object manipulation. Various studies are examined through a survey of existing literature and investigation of various aspects, namely, the intended applications, techniques applied, challenges faced by researchers and recommendations for minimizing obstacles. This review refers to all relevant articles on deep reinforcement learning-based object manipulation and solutions. The object grasping issue is a major manipulation challenge. Object grasping requires detection systems, methods and tools to facilitate efficient and fast agent training. Several studies have proposed that object grasping and its subtypes are the main elements in dealing with the environment and agent. Unlike other review articles, this review article provides different observations on deep reinforcement learning-based manipulation. The results of this comprehensive review of deep reinforcement learning in the manipulation field may be valuable for researchers and practitioners because they can expedite the establishment of important guidelines.

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“…The CPO and PPO are optimized sequentially in the loop, as shown in Line (4)(5). The iterative CPO-PPO will be terminated if both the CPO and PPO are close to convergence or reaching the constraint boundaries, as shown in Line (6)(7). m ∈ Z + denotes an iteration threshold to stop the finger splitting.…”

Section: Iterative Cpo-ppomentioning

confidence: 99%

“…The idea of eigengrasps is described in [5] to simplify the joint space searching, and the grasp is optimized by solving a nonlinear optimization with over one hundred seconds. In [6], the power grasps are planned by sequentially searching the palm pose and contact positions. Instead of optimizing for grasp qualities, several heuristics such as the intersected volume and the finger curling planes are utilized, and the contact point searching assumes planar finger motion and fixed palm locations.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Grasp Planning for Multi-Fingered Hands by Finger Splitting

Tian

Lin

et al. 2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Grasp planning for multi-fingered hands is computationally expensive due to the joint-contact coupling, surface nonlinearities and high dimensionality, thus is generally not affordable for real-time implementations. Traditional planning methods by optimization, sampling or learning work well in planning for parallel grippers but remain challenging for multifingered hands. This paper proposes a strategy called finger splitting, to plan precision grasps for multi-fingered hands starting from optimal parallel grasps. The finger splitting is optimized by a dual-stage iterative optimization including a contact point optimization (CPO) and a palm pose optimization (PPO), to gradually split fingers and adjust both the contact points and the palm pose. The dual-stage optimization is able to consider both the object grasp quality and hand manipulability, address the nonlinearities and coupling, and achieve efficient convergence within one second. Simulation results demonstrate the effectiveness of the proposed approach. The simulation video is available at [1].

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Real-Time Grasping Planning for Robotic Bin-Picking and Kitting Applications

Cited by 28 publications

References 30 publications

Optimal Grasping Pose Synthesis in a Constrained Environment

Optimal Grasping Pose Synthesis in a Constrained Environment

Review of Deep Reinforcement Learning-Based Object Grasping: Techniques, Open Challenges, and Recommendations

Real-Time Grasp Planning for Multi-Fingered Hands by Finger Splitting

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