Model-based coverage motion planning for industrial 3D shape inspection applications

Wei, Jing; Polden, Joseph; Tao, Pey Yuen; Goh, Chun Fan; Lin, Wei; Shimada, Kenji

doi:10.1109/coase.2017.8256278

Cited by 14 publications

(8 citation statements)

References 25 publications

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“…In terms of Model-based approach and Model-free approach, the model-based method proposed by the author can effectively improve the efficiency of inspection tasks in [10], but it lacks the ability to handle unexpected things, such as the external model. Sudden change or robot model change, this method needs to reinitialize the definition.…”

Section: Discussionmentioning

confidence: 99%

“…A model-based motion planning for industrial manipulators with sensors for shape inspection tasks was proposed in [10]. The inspection work using the 6-degree-offreedom industrial manipulator must first complete the determination of a set of viewpoints of an inspected object, also called view planning, and secondly determine the trajectory of the collision-free robot with the best viewpoint, which is called the path planning problem.…”

Section: Model-based and Model-freementioning

confidence: 99%

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A Review of Motion Planning Algorithms for Robotic Arm Systems

Liu

2021

Lecture Notes in Mechanical Engineering

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Motion planning plays a vital role in the field of robotics. This paper discusses the latest advancements made in the research and development of various algorithms and approaches in motion planning in the past five years, with a strong focus on robotic arm systems. Most of the recent motion planning algorithms are based on random sampling algorithms. More effective algorithms such as optimization-based, Probabilistic Movement Primitives (ProMPs)-based and physics-based methods are feasible research directions to explore to improve the effectiveness. The evaluation benchmarking of the algorithm is a worthy research direction. The model-based methods can improve the efficiency of the task, but it has less ability to deal with accidents. In contrast, the modelfreed methods can solve this problem, but it takes a long time to compute. This paper also provides an insight into robotic manipulation of rigid and non-rigid (deformable) objects is explored. Based on the study, some challenges and future research trends are summarized, and some algorithms and approaches are suggested for the most efficient use of the robotic arms.

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Section: Discussionmentioning

confidence: 99%

Section: Model-based and Model-freementioning

confidence: 99%

A Review of Motion Planning Algorithms for Robotic Arm Systems

Liu

2021

Lecture Notes in Mechanical Engineering

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“…Moreover, the discrete viewpoint sampling method is known to be unreliable for registration [11], because the viewing angles from different viewpoints may cause failure in image Additionally, the proposed planning framework is flexible and reconfigurable. Different algorithms/modules could be used to replace the current ones such that the proposed framework is adoptable for other applications, for example, product shape inspection problem with robotic manipulator [31] or mobile robot [13]. Moreover, with properly modeling of the process, it is also possible to extend the proposed planning framework to other planning problem in robotic applications with coverage constraints, such as robotized painting, surface polishing.…”

Section: Discussionmentioning

confidence: 99%

Coverage Path Planning using Path Primitive Sampling and Primitive Coverage Graph for Visual Inspection

Wei¹,

Deng

Xiao

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Planning the path to gather the surface information of the target objects is crucial to improve the efficiency of and reduce the overall cost, for visual inspection applications with Unmanned Aerial Vehicles (UAVs). Coverage Path Planning (CPP) problem is often formulated for these inspection applications because of the coverage requirement. Traditionally, researchers usually plan and optimize the viewpoints to capture the surface information first, and then optimize the path to visit the selected viewpoints. In this paper, we propose a novel planning method to directly sample and plan the inspection path for a camera-equipped UAV to acquire visual and geometric information of the target structures as a video stream setting in complex 3D environment. The proposed planning method first generates via-points and path primitives around the target object by using sampling methods based on voxel dilation and subtraction. A novel Primitive Coverage Graph (PCG) is then proposed to encode the topological information, flying distances, and visibility information, with the sampled via-points and path primitives. Finally graph search is performed to find the resultant path in the PCG to complete the inspection task with the coverage requirements. The effectiveness of the proposed method is demonstrated through simulation and field tests in this paper.

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“…Apart from the sensor-based planning method [112,113], the sampling-based view planning approach [114,115] is another solution for solving the optimization problem, requiring both view planning and motion planning tasks [116,117]. View planning mainly applies to modeling applications and exploration tasks [118].…”

Section: ) View Planning and Motion Planningmentioning

confidence: 99%

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

Mohd‐Mokhtar²,

2021

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The small battery capacities of the mobile robot and the un-optimized planning efficiency of the industrial robot bottlenecked the time efficiency and productivity rate of coverage tasks in terms of speed and accuracy, putting a great constraint on the usability of the robot applications in various planning strategies in specific environmental conditions. Thus, it became highly desirable to address the optimization problems related to exploration and coverage path planning (CPP). In general, the goal of the CPP is to find an optimal coverage path with generates a collision-free trajectory by reducing the travel time, processing speed, cost energy, and the number of turns along the path length, as well as low overlapped rate, which reflect the robustness of CPP. This paper reviews the principle of CPP and discusses the development trend, including design variations and the characteristic of optimization algorithms, such as classical, heuristic, and most recent deep learning methods. Then, we compare the advantages and disadvantages of the existing CPP-based modeling in the area and target coverage. Finally, we conclude numerous open research problems of the CPP and make suggestions for future research directions to gain insights. INDEX TERMSCoverage path planning, exploration, heuristic algorithm, deep reinforcement learning.

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Model-based coverage motion planning for industrial 3D shape inspection applications

Cited by 14 publications

References 25 publications

A Review of Motion Planning Algorithms for Robotic Arm Systems

A Review of Motion Planning Algorithms for Robotic Arm Systems

Coverage Path Planning using Path Primitive Sampling and Primitive Coverage Graph for Visual Inspection

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

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