Rapid Replanning in Consecutive Pick-and-Place Tasks with Lazy Experience Graph

Lai, Tin; Ramos, Fábio

doi:10.48550/arxiv.2109.10209

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…In structured scenarios, robotic arms often only need to repeat the demonstration trajectory to perform repetitive tasks. However, in a semi-structured scene, the appearance of new obstacles will change the workspace, so that the denomination trajectory may collide with obstacles and affect the execution of operation tasks [ 2 , 3 ]. Furthermore, in some scenarios, the robotic arm often needs to perform multiple picking and placing tasks, the nature of repetitive pick-and-place tasks suggests that the solutions for all motion planning instances are similar to some extent [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Motion Planning Method with a Lazy Demonstration Graph for Repetitive Pick-and-Place

Zuo

et al. 2022

Biomimetics

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Robotic systems frequently need to plan consecutive similar manipulation in some scenarios (e.g., pick-and-place tasks), leading to similar motion plans. Moreover, the workspace of a robot changes with the difference in operation actions, which affects subsequent tasks. Therefore, it is significant to reuse information from previous solutions for new motion planning instances to adapt to workplace changes. This paper proposes the Lazy Demonstration Graph (LDG) planner, a novel motion planner that exploits successful and high-quality planning cases as prior knowledge. In addition, a Gaussian Mixture Model (GMM) is established by learning the distribution of samples in the planning cases. Through the trained GMM, more samples are placed in a promising location related to the planning tasks for achieving the purpose of adaptive sampling. This adaptive sampling strategy is combined with the Lazy Probabilistic Roadmap (LazyPRM) algorithm; in the subsequent planning tasks, this paper uses the multi-query property of a road map to solve motion planning problems without planning from scratch. The lazy collision detection of the LazyPRM algorithm helps overcome changes in the workplace by searching candidate paths. Our method also improves the quality and success rate of the path planning of LazyPRM. Compared with other state-of-the-art motion planning algorithms, our method achieved better performance in the planning time and path quality. In the repetitive motion planning experiment of the manipulator for pick-and-place tasks, we designed two different experimental scenarios in the simulation environment. The physical experiments are also carried out in AUBO−i5 robot arm. Accordingly, the experimental results verified our method’s validity and robustness.

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Section: Introductionmentioning

confidence: 99%

An Efficient Motion Planning Method with a Lazy Demonstration Graph for Repetitive Pick-and-Place

Zuo

et al. 2022

Biomimetics

View full text Add to dashboard Cite

show abstract

“…Example include planning with arbitrary cost maps (Iehl et al, 2012), cooperative multi-agent planning (Jiang & Wu, 2020), and planning in dynamic environments (Yershova et al, 2005). On the one hand, researchers have focused on the algorithmic side of improving the graph or tree building (Elbanhawi & Simic, 2014;Klemm et al, 2015;Lai et al, 2019;Lai & Ramos, 2021b;Zhong & Su, 2012). On the other hand, the advancement of neural networks allows an abundance of learning approaches to be applied in SBPs (Bagnell, 2014;Strub & Gammell, 2020) and on improving the sampling distribution (Alcin et al, 2016;, 2021a.…”

Section: Introductionmentioning

confidence: 99%

sbp-env: Sampling-based Motion Planners' Testing Environment

Lai

2021

Preprint

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Rapid Replanning in Consecutive Pick-and-Place Tasks with Lazy Experience Graph

Cited by 2 publications

References 23 publications

An Efficient Motion Planning Method with a Lazy Demonstration Graph for Repetitive Pick-and-Place

An Efficient Motion Planning Method with a Lazy Demonstration Graph for Repetitive Pick-and-Place

sbp-env: Sampling-based Motion Planners' Testing Environment

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