A distributed variable density path search and simplification method for industrial manipulators with end-effector’s attitude constraints

Jin WANG; Shengjie LI; Haiyun ZHANG; Guodong LU; Yichang FENG; Peng WANG; Jituo LI

doi:10.1631/FITEE.2200353

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A distributed variable density path search and simplification method for industrial manipulators with end-effector’s attitude constraints

Regular Papers | Updated：2023-05-08

- A distributed variable density path search and simplification method for industrial manipulators with end-effector’s attitude constraints
- 一种满足末端姿态约束的工业机械臂分布式变密度路径搜索与简化方法
- Frontiers of Information Technology & Electronic Engineering Vol. 24, Issue 4, Pages: 536-552(2023)
- Affiliations：
  
  1.State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China
  2.Engineering Research Center for Design Engineering and Digital Twin of Zhejiang Province, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China
  3.School of Robotics, Ningbo University of Technology, Ningbo 315211, China
- Author bio：
  
  ‡Corresponding author
- Funds：
- DOI：10.1631/FITEE.2200353
  CLC： TP242
- Received：21 August 2022，
  
  Revised：2022-12-07，
  
  Published：0 April 2023
- Accepted：
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Jin WANG, Shengjie LI, Haiyun ZHANG, et al. A distributed variable density path search and simplification method for industrial manipulators with end-effector’s attitude constraints[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(4): 536-552.
DOI：

Jin WANG, Shengjie LI, Haiyun ZHANG, et al. A distributed variable density path search and simplification method for industrial manipulators with end-effector’s attitude constraints[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(4): 536-552. DOI： 10.1631/FITEE.2200353.

摘要

在许多机器人操作场景中，末端执行器的运动姿态约束是机器人完成焊接、喷涂、搬运、码垛等常见任务必不可少的。同时，姿态约束下的路径规划过程中涉及到逆运动学、碰撞检测和空间搜索等关键问题，在实际应用中难以兼顾令人满意的效率和约束效果。针对这些问题，提出一种带末端约束的工业机器人分布式变密度路径规划方法（DVDP-AC）。首先，针对运动学逆解提出位置–姿态约束重构（PACR）方法。然后，设计了具有单步安全球（SSS）的分布式有向距离场（DSDF）模型，以提高碰撞检测的效率。在此基础上，在笛卡尔空间中采用变密度路径搜索方法，并进一步提出一种考虑路径可达性的前向路径简化方法（FSPS），以自适应地快速消除冗余的路径点。最后，实验结果验证了所提出的DVDP-AC方法在末端执行器姿态约束下的性能和有效性，并与目前主流路径规划方法进行比较，说明了该方法的特点和优势。

Abstract

In many robot operation scenarios

the end-effector’s attitude constraints of movement are indispensable for the task process

such as robotic welding

spraying

handling

and stacking. Meanwhile

the inverse kinematics

collision detection

and space search are involved in the path planning procedure under attitude constraints

making it difficult to achieve satisfactory efficiency and effectiveness in practice. To address these problems

we propose a distributed variable density path planning method with attitude constraints (DVDP-AC) for industrial robots. First

a position–attitude constraints reconstruction (PACR) approach is proposed in the inverse kinematic solution. Then

the distributed signed-distance-field (DSDF) model with single-step safety sphere (SSS) is designed to improve the efficiency of collision detection. Based on this

the variable density path search method is adopted in the Cartesian space. Furthermore

a novel forward sequential path simplification (FSPS) approach is proposed to adaptively eliminate redundant path points considering path accessibility. Finally

experimental results verify the performance and effectiveness of the proposed DVDP-AC method under end-effector’s attitude constraints

and its characteristics and advantages are demonstrated by comparison with current mainstream path planning methods.

关键词

Keywords

references

Abele E , Haehn F , Pischan M , et al. , 2016 . Time optimal path planning for industrial robots using STL data files . Proc CIRP , 55 : 6 - 11 . https://doi.org/10.1016/j.procir.2016.08.038 https://doi.org/10.1016/j.procir.2016.08.038

Adeli H , Tabrizi MHN , Mazloomian A , et al. , 2011 . Path planning for mobile robots using iterative artificial potential field method . Int J Comput Sci Iss , 8 ( 4 ): 28 - 32 .

Ademovic A , Lacevic B , 2014 . Path planning for robotic manipulators via bubbles of free configuration space: evolutionary approach . Proc 22 nd Mediterranean Conf on Control and Automation , p. 1323 - 1328 . https://doi.org/10.1109/MED.2014.6961559 https://doi.org/10.1109/MED.2014.6961559

Baziyad M , Saad M , Fareh R , et al. , 2021 . Addressing real-time demands for robotic path planning systems: a routing protocol approach . IEEE Access , 9 : 38132 - 38143 . https://doi.org/10.1109/ACCESS.2021.3058121 https://doi.org/10.1109/ACCESS.2021.3058121

Dijkstra EW , 1959 . A note on two problems in connexion with graphs . Numer Math , 1 ( 1 ): 269 - 271 . https://doi.org/10.1007/BF01386390 https://doi.org/10.1007/BF01386390

Ferguson D , Stentz A , 2006 . Using interpolation to improve path planning: the field D* algorithm . J Field Robot , 23 ( 2 ): 79 - 101 . https://doi.org/10.1002/rob.20109 https://doi.org/10.1002/rob.20109

Fu B , Chen L , Zhou YT , et al. , 2018 . An improved A * algorithm for the industrial robot path planning with high success rate and short length . Robot Auton Syst , 106 : 26 - 37 . https://doi.org/10.1016/j.robot.2018.04.007 https://doi.org/10.1016/j.robot.2018.04.007

Gottschalk S , Lin MC , Manocha D , 1996 . OBBtree: a hierarchical structure for rapid interference detection . Proc 23 rd Annual Conf on Computer Graphics and Interactive Techniques , p. 171 - 180 . https://doi.org/10.1145/237170.237244 https://doi.org/10.1145/237170.237244

Han D , Nie H , Chen JB , et al. , 2018 . Dynamic obstacle avoidance for manipulators using distance calculation and discrete detection . Robot Comput-Integr Manuf , 49 : 98 - 104 . https://doi.org/10.1016/j.rcim.2017.05.013 https://doi.org/10.1016/j.rcim.2017.05.013

Harik GR , Lobo FG , Goldberg DE , 1999 . The compact genetic algorithm . IEEE Trans Evol Comput , 3 ( 4 ): 287 - 297 . https://doi.org/10.1109/4235.797971 https://doi.org/10.1109/4235.797971

Hart PE , Nilsson NJ , Raphael B , 1968 . A formal basis for the heuristic determination of minimum cost paths . IEEE Trans Syst Sci Cybern , 4 ( 2 ): 100 - 107 . https://doi.org/10.1109/TSSC.1968.300136 https://doi.org/10.1109/TSSC.1968.300136

Hernández C , Baier JA , Asín R , 2014 . Making A ∗ run faster than D ∗ -lite for path-planning in partially known terrain . Proc 24 th Int Conf on Automated Planning and Scheduling , p. 504 - 508 .

Huo XJ , Liu YW , Jiang L , et al. , 2014 . Inverse kinematic optimizations of 7R humanoid arms based on a joint parameterization . IEEE Int Conf on Mechatronics and Automation , p. 113 - 118 . https://doi.org/10.1109/ICMA.2014.6885681 https://doi.org/10.1109/ICMA.2014.6885681

Janson L , Schmerling E , Clark A , et al. , 2015 . Fast marching tree: a fast marching sampling-based method for optimal motion planning in many dimensions . Int J Robot Res , 34 ( 7 ): 883 - 921 . https://doi.org/10.1177/0278364915577958 https://doi.org/10.1177/0278364915577958

Kalakrishnan M , Chitta S , Theodorou E , et al. , 2011 . STOMP: stochastic trajectory optimization for motion planning . IEEE Int Conf on Robotics and Automation , p. 9 - 13 . https://doi.org/10.1109/ICRA.2011.5980280 https://doi.org/10.1109/ICRA.2011.5980280

Klingensmith M , Dryanovski I , Srinivasa S , et al. , 2015 . CHISEL: real time large scale 3D reconstruction onboard a mobile device using spatially-hashed signed distance fields . Proc Robotics: Science and Systems , Article11 . https://doi.org/10.15607/RSS.2015.XI.040 https://doi.org/10.15607/RSS.2015.XI.040

Koenig S , Likhachev M , 2005 . Fast replanning for navigation in unknown terrain . IEEE Trans Robot , 21 ( 3 ): 354 - 363 . https://doi.org/10.1109/TRO.2004.838026 https://doi.org/10.1109/TRO.2004.838026

Koenig S , Likhachev M , Furcy D , 2004 . Lifelong planning A * . Artif Intell , 155 ( 1-2 ): 93 - 146 . https://doi.org/10.1016/j.artint.2003.12.001 https://doi.org/10.1016/j.artint.2003.12.001

Kuffner JJ , LaValle SM , 2000 . RRT-connect: an efficient approach to single-query path planning . Proc IEEE Int Conf on Robotics and Automation , p. 995 - 1001 . https://doi.org/10.1109/robot.2000.844730 https://doi.org/10.1109/robot.2000.844730

LaValle SM , 1998 . Rapidly-Exploring Random Trees: a New Tool for Path Planning . Technical Report, TR98-11 , Department of Computer Science, lowa State University , Ames, USA .

Li SP , Wang ZJ , Zhang Q , et al. , 2018 . Solving inverse kinematics model for 7-DoF robot arms based on space vector . Int Conf on Control and Robots , p. 1 - 5 . https://doi.org/10.1109/ICCR.2018.8534498 https://doi.org/10.1109/ICCR.2018.8534498

Liu HS , Zhang Y , Zhu SQ , 2015 . Novel inverse kinematic approaches for robot manipulators with Pieper-Criterion based geometry . Int J Contr Autom Syst , 13 ( 5 ): 1242 - 1250 . https://doi.org/10.1007/s12555-013-0440-y https://doi.org/10.1007/s12555-013-0440-y

Liu YY , Xi JL , Bai HF , et al. , 2021 . A general robot inverse kinematics solution method based on improved PSO algorithm . IEEE Access , 9 : 32341 - 32350 . https://doi.org/10.1109/ACCESS.2021.3059714 https://doi.org/10.1109/ACCESS.2021.3059714

Persson SM , Sharf I , 2014 . Sampling-based A ∗ algorithm for robot path-planning . Int J Robot Res , 33 ( 13 ): 1683 - 1708 . https://doi.org/10.1177/0278364914547786 https://doi.org/10.1177/0278364914547786

Qureshi AH , Ayaz Y , 2016 . Potential functions based sampling heuristic for optimal path planning . Auton Robots , 40 ( 6 ): 1079 - 1093 .

Starek JA , Gomez JV , Schmerling E , et al. , 2015 . An asymptotically-optimal sampling-based algorithm for bi-directional motion planning . IEEE/RSJ Int Conf on Intelligent Robots and Systems , p. 2072 - 2078 . https://doi.org/10.1109/IROS.2015.7353652 https://doi.org/10.1109/IROS.2015.7353652

Sun XX , Yeoh W , Koenig S , 2010 . Moving target D* lite . Proc 9 th Int Conf on Autonomous Agents and Multiagent Systems , p. 67 - 74 .

Tan T , Weller R , Zachmann G , 2020 . Compressed bounding volume hierarchies for collision detection & proximity query . https://doi.org/10.48550/arXiv.2012.05348 https://doi.org/10.48550/arXiv.2012.05348

Xie YM , Zhou R , Yang YS , 2020 . Improved distorted configuration space path planning and its application to robot manipulators . Sensors , 20 ( 21 ): 6060 . https://doi.org/10.3390/s20216060 https://doi.org/10.3390/s20216060

Xing YS , Liu XP , Xu SP , 2010 . Efficient collision detection based on AABB trees and sort algorithm . 8 th IEEE Int Conf on Control and Automation , p. 328 - 332 . https://doi.org/10.1109/ICCA.2010.5524093 https://doi.org/10.1109/ICCA.2010.5524093

Zucker M , Ratliff N , Dragan AD , et al. , 2013 . CHOMP: covariant Hamiltonian optimization for motion planning . Int J Robot Res , 32 ( 9-10 ): 1164 - 1193 . https://doi.org/10.1177/0278364913488805 https://doi.org/10.1177/0278364913488805

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