Semi-lazy probabilistic roadmap: a parameter-tuned, resilient and robust path planning method for manipulator robots

An indispensable feature of a modern intelligent robot is its capability to plan short and safe motions in the presence of obstacles in its workspace, which is highly important for industrial manipulators in charge of automatic picking and placing, welding, painting, etc. On the other hand, collisio...

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Vydané v:	International journal of advanced manufacturing technology Ročník 89; číslo 5-8; s. 1401 - 1430
Hlavní autori:	Akbaripour, Hossein, Masehian, Ellips
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Springer London 01.03.2017 Springer Nature B.V
Predmet:	Algorithms Automatic welding Barriers CAE) and Design Collision avoidance Computer simulation Computer-Aided Engineering (CAD End effectors Engineering Entropy (Information theory) Industrial and Production Engineering Manipulators Mechanical Engineering Media Management Motion planning Original Article Parameter robustness Path planning Planning Probabilistic methods Probability theory Road construction Robot arms Robustness Sampling Workspace Motion planning Collision checking strategy Parameter tuning Manipulator robot Semi-lazy probabilistic roadmap Robustness Resilience
ISSN:	0268-3768, 1433-3015
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Abstract	An indispensable feature of a modern intelligent robot is its capability to plan short and safe motions in the presence of obstacles in its workspace, which is highly important for industrial manipulators in charge of automatic picking and placing, welding, painting, etc. On the other hand, collision-free motion planning of serial manipulators becomes exponentially hard with the increase of number of joints, and so efficient methods like sampling-based ones are vastly used for most real-world problems. In this paper, we propose a new variation of sampling-based methods called semi-lazy probabilistic roadmap (SLPRM) for motion planning of industrial manipulators, which benefits from the advantages of the basic probabilistic roadmap (PRM) and lazy-PRM (LPRM) methods. Unlike the exhaustive and zero collision-checking policies implemented respectively in PRM and LPRM, the SLPRM collision-checks random configurations for only m terminal links (i.e., from end-effector backwards) of the manipulator in the roadmap construction phase. As a result, on one hand, the roadmap construction time reduces compared with PRM due to less collision checks, and on the other hand, query times decrease compared with LPRM due to a better quality of the initial roadmap. A central decision in SLPRM is to properly determine the value of m , which has a direct effect on its speed. For this purpose, a new parameter tuning approach based on a combination of Shannon’s Entropy and VIKOR methods is implemented to determine the best values for m and all other parameters of the algorithm. The proposed method has been tested and implemented in simulated and real workspace scenarios for an RV-E3J Mitsubishi industrial manipulator robot, and the results showed that the mean planning time of the SLPRM was shorter compared with that of the PRM and LPRM. To make the algorithm resilient and robust to internal faults and environmental variations such as positional errors, joint failures, and obstacle displacements, we have also proposed the resilient and robust SLPRM, which through concentrated sampling and roadmap-amending procedures, can handle unexpected failures and changes.
AbstractList	An indispensable feature of a modern intelligent robot is its capability to plan short and safe motions in the presence of obstacles in its workspace, which is highly important for industrial manipulators in charge of automatic picking and placing, welding, painting, etc. On the other hand, collision-free motion planning of serial manipulators becomes exponentially hard with the increase of number of joints, and so efficient methods like sampling-based ones are vastly used for most real-world problems. In this paper, we propose a new variation of sampling-based methods called semi-lazy probabilistic roadmap (SLPRM) for motion planning of industrial manipulators, which benefits from the advantages of the basic probabilistic roadmap (PRM) and lazy-PRM (LPRM) methods. Unlike the exhaustive and zero collision-checking policies implemented respectively in PRM and LPRM, the SLPRM collision-checks random configurations for only m terminal links (i.e., from end-effector backwards) of the manipulator in the roadmap construction phase. As a result, on one hand, the roadmap construction time reduces compared with PRM due to less collision checks, and on the other hand, query times decrease compared with LPRM due to a better quality of the initial roadmap. A central decision in SLPRM is to properly determine the value of m, which has a direct effect on its speed. For this purpose, a new parameter tuning approach based on a combination of Shannon’s Entropy and VIKOR methods is implemented to determine the best values for m and all other parameters of the algorithm. The proposed method has been tested and implemented in simulated and real workspace scenarios for an RV-E3J Mitsubishi industrial manipulator robot, and the results showed that the mean planning time of the SLPRM was shorter compared with that of the PRM and LPRM. To make the algorithm resilient and robust to internal faults and environmental variations such as positional errors, joint failures, and obstacle displacements, we have also proposed the resilient and robust SLPRM, which through concentrated sampling and roadmap-amending procedures, can handle unexpected failures and changes. An indispensable feature of a modern intelligent robot is its capability to plan short and safe motions in the presence of obstacles in its workspace, which is highly important for industrial manipulators in charge of automatic picking and placing, welding, painting, etc. On the other hand, collision-free motion planning of serial manipulators becomes exponentially hard with the increase of number of joints, and so efficient methods like sampling-based ones are vastly used for most real-world problems. In this paper, we propose a new variation of sampling-based methods called semi-lazy probabilistic roadmap (SLPRM) for motion planning of industrial manipulators, which benefits from the advantages of the basic probabilistic roadmap (PRM) and lazy-PRM (LPRM) methods. Unlike the exhaustive and zero collision-checking policies implemented respectively in PRM and LPRM, the SLPRM collision-checks random configurations for only m terminal links (i.e., from end-effector backwards) of the manipulator in the roadmap construction phase. As a result, on one hand, the roadmap construction time reduces compared with PRM due to less collision checks, and on the other hand, query times decrease compared with LPRM due to a better quality of the initial roadmap. A central decision in SLPRM is to properly determine the value of m , which has a direct effect on its speed. For this purpose, a new parameter tuning approach based on a combination of Shannon’s Entropy and VIKOR methods is implemented to determine the best values for m and all other parameters of the algorithm. The proposed method has been tested and implemented in simulated and real workspace scenarios for an RV-E3J Mitsubishi industrial manipulator robot, and the results showed that the mean planning time of the SLPRM was shorter compared with that of the PRM and LPRM. To make the algorithm resilient and robust to internal faults and environmental variations such as positional errors, joint failures, and obstacle displacements, we have also proposed the resilient and robust SLPRM, which through concentrated sampling and roadmap-amending procedures, can handle unexpected failures and changes.
Author	Akbaripour, Hossein Masehian, Ellips
Author_xml	– sequence: 1 givenname: Hossein surname: Akbaripour fullname: Akbaripour, Hossein organization: Faculty of Industrial and Systems Engineering, Tarbiat Modares University – sequence: 2 givenname: Ellips surname: Masehian fullname: Masehian, Ellips email: masehian@modares.ac.ir organization: Faculty of Industrial and Systems Engineering, Tarbiat Modares University
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Copyright	Springer-Verlag London 2016 Copyright Springer Science & Business Media 2017 The International Journal of Advanced Manufacturing Technology is a copyright of Springer, (2016). All Rights Reserved. Springer-Verlag London 2016.
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Snippet	An indispensable feature of a modern intelligent robot is its capability to plan short and safe motions in the presence of obstacles in its workspace, which is...
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SubjectTerms	Algorithms Automatic welding Barriers CAE) and Design Collision avoidance Computer simulation Computer-Aided Engineering (CAD End effectors Engineering Entropy (Information theory) Industrial and Production Engineering Manipulators Mechanical Engineering Media Management Motion planning Original Article Parameter robustness Path planning Planning Probabilistic methods Probability theory Road construction Robot arms Robustness Sampling Workspace
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Title	Semi-lazy probabilistic roadmap: a parameter-tuned, resilient and robust path planning method for manipulator robots
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