Effective motion planning of manipulator based on SDPS-RRTConnect

Xu, Junxiang; Wang, Jiwu

doi:10.1017/s0263574721001417

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…Although this method is faster than RRT, they both often produce suboptimal paths in practice and the quality of the path depends on the density of samples and the shape of the configuration space. In order to enhance the performance of the Bi-RRT method for manipulators, a pioneering study by Xu et al [21] presents a novel modified version of this technique. The key innovation of their work lies in the integration of a sparse dead point saved strategy, effectively reducing the frequency of collision checks.…”

Section: Related Workmentioning

confidence: 99%

End-to-end deep learning-based framework for path planning and collision checking: bin-picking application

Ghafarian Tamizi,

Honari,

Nozdryn-Plotnicki

et al. 2024

Robotica

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Real-time and efficient path planning is critical for all robotic systems. In particular, it is of greater importance for industrial robots since the overall planning and execution time directly impact the cycle time and automation economics in production lines. While the problem may not be complex in static environments, classical approaches are inefficient in high-dimensional environments in terms of planning time and optimality. Collision checking poses another challenge in obtaining a real-time solution for path planning in complex environments. To address these issues, we propose an end-to-end learning-based framework viz., Path Planning and Collision checking Network (PPCNet). The PPCNet generates the path by computing waypoints sequentially using two networks: the first network generates a waypoint, and the second one determines whether the waypoint is on a collision-free segment of the path. The end-to-end training process is based on imitation learning that uses data aggregation from the experience of an expert planner to train the two networks, simultaneously. We utilize two approaches for training a network that efficiently approximates the exact geometrical collision checking function. Finally, the PPCNet is evaluated in two different simulation environments and a practical implementation on a robotic arm for a bin-picking application. Compared to the state-of-the-art path-planning methods, our results show significant improvement in performance by greatly reducing the planning time with comparable success rates and path lengths.

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Section: Related Workmentioning

confidence: 99%

End-to-end deep learning-based framework for path planning and collision checking: bin-picking application

Ghafarian Tamizi,

Honari,

Nozdryn-Plotnicki

et al. 2024

Robotica

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“…In ref. [22], the authors proposed an improved RRTConnect algorithm based on sparse dead point saved strategy to improve the speed of motion planning, and their experimental studies show the strong robustness. Polvara et al [23] employed the sparse reward for the proposed Deep Q-Networks to be the high-level navigation policy of the landing problem of an unmanned aerial vehicle, the simulation works validated their proposal with great robustness.…”

Section: Introductionmentioning

confidence: 99%

A sparsity-based method for fault-tolerant manipulation of a redundant robot

et al. 2022

Robotica

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As an important part of the manufacturing industry, redundant robots can undertake heavy and tough tasks, which human operators are difficult to sustain. Such onerous and repetitive industrial manipulations, that is, positioning and carrying, impose heavy burdens on the load bearing for redundancy robots’ joints. Under the circumstances of long-term and intense industrial operations, joints of redundant robots are conceivably to fall into functional failure, which may possibly cause abrupt joint lock or freeze at unknown time instants. Therefore, task accuracy by end-effectors tends to diminish considerably and gradually because of broken-down joints. In this paper, a sparsity-based method for fault-tolerant motion planning of redundant robots is provided for the first time. The developed fault-tolerant redundancy resolution approach is defined as L1-norm based optimization with immediate variables involved to avoid discontinuity in the dynamic solution process. Meanwhile, those potential faulty joint(s) can be located by the designed fault observer with the proposed fault-diagnosis algorithm. The proposed fault-tolerant motion planning method with fault diagnosis is dynamically optimized by resultant primal dual neural networks with provable convergence. Moreover, the sparsity of joint actuation by the proposed method can be enhanced by around 43.87% and 36.51%, respectively, for tracking circle and square paths. Simulation and experimental findings on a redundant robot (KUKA iiwa) prove the efficacy of the developed defect tolerant approach based on sparsity.

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“…Xu et al. proposed an RRTConnect algorithm based on a sparse expansion strategy and dead point saved strategy [ 12 ], which can effectively reduce the number of collision detections and accelerate the convergence speed. In contrast, our approach leverages past planning experience and can further reduce the likelihood of redundant sampling.…”

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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Effective motion planning of manipulator based on SDPS-RRTConnect

Cited by 4 publications

References 18 publications

End-to-end deep learning-based framework for path planning and collision checking: bin-picking application

End-to-end deep learning-based framework for path planning and collision checking: bin-picking application

A sparsity-based method for fault-tolerant manipulation of a redundant robot

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

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