Evidence map›Paper›PMID 36236645›Full record

ArticleSensors (Basel, Switzerland)2022

Energy Saving Planner Model via Differential Evolutionary Algorithm for Bionic Palletizing Robot.

Yi Deng, Tao Zhou, Guojin Zhao, Kuihu Zhu, Zhaixin Xu, Hai Liu

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.8field-weighted citation impact, top 32% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 10 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 3 institutions in 1 country.

Yi DengSchool of Electronic and Electrical Engineering, Wuhan Textile University, Wuhan 430200, China.
Tao ZhouSchool of Electronic and Electrical Engineering, Wuhan Textile University, Wuhan 430200, China.
Guojin ZhaoSchool of Electronic and Electrical Engineering, Wuhan Textile University, Wuhan 430200, China.
Kuihu ZhuSchool of Electronic and Electrical Engineering, Wuhan Textile University, Wuhan 430200, China.
Zhaixin XuDepartment of Research and Development, TBEA Co., Ltd., Changji 100089, China.
Hai LiuSchool of Electronic and Electrical Engineering, Wuhan Textile University, Wuhan 430200, China.
Wuhan Textile University · CNTebian Electric Apparatus (China) · CNCentral China Normal University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Energy saving in palletizing robot is a fundamental problem in the field of industrial robots. However, the palletizing robot often suffers from the problems of high energy consumption and lacking flexibility. In this work, we introduce a novel differential evolution algorithm to address the adverse effects caused by the instability of the initial trajectory parameters while reducing the energy. Specially, a simplified analytical model of the palletizing robot is firstly developed. Then, the simplified analytical model and the differential evolutionary algorithm are combined to form a planner with the goal of reducing energy consumption. The energy saving planner optimizes the initial parameters of the trajectories collected by the bionic demonstration system, which in turn enables a reduction in the operating power consumption of the palletizing robot. The major novelty of this article is the use of a differential evolutionary algorithm that can save the energy consumption as well as boosting its flexibility. Comparing with the traditional algorithms, the proposed method can achieve the state-of-the-art performance. Simulated and actual experimental results illustrate that the optimized trajectory parameters can effectively reduce the energy consumption of palletizing robot by 16%.

Indexed as

RoboticsAlgorithmsBionicsPhysical Phenomenabionic demonstration systemdifferential evolutionary algorithmoptimization of energypalletizing robot

Identifiers

PMID36236645
PMCPMC9573082
OpenAlexW4303981150

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.