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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
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A distributed variable density path search and simplification method for industrial manipulators with end-effector’s attitude constraints
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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.
@article{title="A distributed variable density path search and simplification method for industrial manipulators with end-effector’s attitude constraints",
author="Jin WANG, Shengjie LI, Haiyun ZHANG, Guodong LU, Yichang FENG, Peng WANG, Jituo LI",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="24",
number="4",
pages="536-552",
year="2023",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200353"
}
%0 Journal Article
%T A distributed variable density path search and simplification method for industrial manipulators with end-effector’s attitude constraints
%A Jin WANG
%A Shengjie LI
%A Haiyun ZHANG
%A Guodong LU
%A Yichang FENG
%A Peng WANG
%A Jituo LI
%J Frontiers of Information Technology & Electronic Engineering
%V 24
%N 4
%P 536-552
%@ 2095-9184
%D 2023
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200353
TY - JOUR
T1 - A distributed variable density path search and simplification method for industrial manipulators with end-effector’s attitude constraints
A1 - Jin WANG
A1 - Shengjie LI
A1 - Haiyun ZHANG
A1 - Guodong LU
A1 - Yichang FENG
A1 - Peng WANG
A1 - Jituo LI
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 24
IS - 4
SP - 536
EP - 552
%@ 2095-9184
Y1 - 2023
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2200353
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.
[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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