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CLC number: TP242

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 2023-05-06

Cited: 0

Clicked: 1932

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Jin Wang

https://orcid.org/0000-0003-3106-021X

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Frontiers of Information Technology & Electronic Engineering  2023 Vol.24 No.4 P.536-552

http://doi.org/10.1631/FITEE.2200353


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


Author(s):  Jin WANG, Shengjie LI, Haiyun ZHANG, Guodong LU, Yichang FENG, Peng WANG, Jituo LI

Affiliation(s):  State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   dwjcom@zju.edu.cn

Key Words:  Path planning, Industrial robots, Distributed signed-distance-field, Attitude constraints, Path simplification


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

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author="Jin WANG, Shengjie LI, Haiyun ZHANG, Guodong LU, Yichang FENG, Peng WANG, Jituo LI",
journal="Frontiers of Information Technology & Electronic Engineering",
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number="4",
pages="536-552",
year="2023",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200353"
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%A Jin WANG
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A1 - Yichang FENG
A1 - Peng WANG
A1 - Jituo LI
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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.

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

王进1,2,厉圣杰1,2,张海运3,陆国栋1,2,冯奕畅1,2,王鹏1,2,李基拓1,2
1浙江大学机械工程学院流体动力与机电系统国家重点实验室,中国杭州市,310027
2浙江大学机械工程学院设计工程及数字孪生浙江省工程研究中心,中国杭州市,310027
3宁波工程学院机器人学院,中国宁波市,315211
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。

关键词:路径规划;工业机器人;分布式有向距离场;姿态约束;路径简化

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Reference

[1]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.

[2]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.

[3]Ademovic A, Lacevic B, 2014. Path planning for robotic manipulators via bubbles of free configuration space: evolutionary approach. Proc 22nd Mediterranean Conf on Control and Automation, p.1323-1328.

[4]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.

[5]Dijkstra EW, 1959. A note on two problems in connexion with graphs. Numer Math, 1(1):269-271.

[6]Ferguson D, Stentz A, 2006. Using interpolation to improve path planning: the field D* algorithm. J Field Robot, 23(2):79-101.

[7]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.

[8]Gottschalk S, Lin MC, Manocha D, 1996. OBBtree: a hierarchical structure for rapid interference detection. Proc 23rd Annual Conf on Computer Graphics and Interactive Techniques, p.171-180.

[9]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.

[10]Harik GR, Lobo FG, Goldberg DE, 1999. The compact genetic algorithm. IEEE Trans Evol Comput, 3(4):287-297.

[11]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.

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

[13]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.

[14]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.

[15]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.

[16]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.

[17]Koenig S, Likhachev M, 2005. Fast replanning for navigation in unknown terrain. IEEE Trans Robot, 21(3):354-363.

[18]Koenig S, Likhachev M, Furcy D, 2004. Lifelong planning A*. Artif Intell, 155(1-2):93-146.

[19]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.

[20]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.

[21]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.

[22]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.

[23]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.

[24]Persson SM, Sharf I, 2014. Sampling-based A algorithm for robot path-planning. Int J Robot Res, 33(13):1683-1708.

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

[26]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.

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

[28]Tan T, Weller R, Zachmann G, 2020. Compressed bounding volume hierarchies for collision detection & proximity query.

[29]Xie YM, Zhou R, Yang YS, 2020. Improved distorted configuration space path planning and its application to robot manipulators. Sensors, 20(21):6060.

[30]Xing YS, Liu XP, Xu SP, 2010. Efficient collision detection based on AABB trees and sort algorithm. 8th IEEE Int Conf on Control and Automation, p.328-332.

[31]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.

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