Path Planning for Special Robotic Operations

Boscariol, Paolo; Gasparetto, Alessandro; Scalera, Lorenzo

doi:10.1007/978-3-031-11128-0_4

Cited by 7 publications

(6 citation statements)

References 139 publications

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“…Pioneering collision-free path planning for robotic arms engaged in a wide spectrum of tasks, ranging from welding in industrial contexts to delicate medical applications, remains a central focal point in the realm of robotics [4]. path-planning methods can broadly be classified into two categories: classical and learning-based [5].…”

Section: Path Planningmentioning

confidence: 99%

End-to-end deep learning-based framework for path planning and collision checking: bin-picking application

Ghafarian Tamizi,

Honari,

Nozdryn-Plotnicki

et al. 2024

Robotica

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Real-time and efficient path planning is critical for all robotic systems. In particular, it is of greater importance for industrial robots since the overall planning and execution time directly impact the cycle time and automation economics in production lines. While the problem may not be complex in static environments, classical approaches are inefficient in high-dimensional environments in terms of planning time and optimality. Collision checking poses another challenge in obtaining a real-time solution for path planning in complex environments. To address these issues, we propose an end-to-end learning-based framework viz., Path Planning and Collision checking Network (PPCNet). The PPCNet generates the path by computing waypoints sequentially using two networks: the first network generates a waypoint, and the second one determines whether the waypoint is on a collision-free segment of the path. The end-to-end training process is based on imitation learning that uses data aggregation from the experience of an expert planner to train the two networks, simultaneously. We utilize two approaches for training a network that efficiently approximates the exact geometrical collision checking function. Finally, the PPCNet is evaluated in two different simulation environments and a practical implementation on a robotic arm for a bin-picking application. Compared to the state-of-the-art path-planning methods, our results show significant improvement in performance by greatly reducing the planning time with comparable success rates and path lengths.

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Section: Path Planningmentioning

confidence: 99%

End-to-end deep learning-based framework for path planning and collision checking: bin-picking application

Ghafarian Tamizi,

Honari,

Nozdryn-Plotnicki

et al. 2024

Robotica

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“…The advantages and disadvantages of path planning techniques from the point of view of special robotic operations were reviewed in [39]. Several approaches to analyzing the safety and feasibility in path planning were investigated and compared, considering their role in contexts involving underwater vehicles, Unmanned Aerial Vehicle (UAV)s, Autonomous Guided Vehicle (AGV)s, and industrial robots, with applications in mountainous areas, warehouses, and production lines.…”

Section: Related Work and Taxonomymentioning

confidence: 99%

Conventional, Heuristic and Learning-Based Robot Motion Planning: Reviewing Frameworks of Current Practical Significance

2023

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Motion planning algorithms have seen considerable progress and expansion across various domains of science and technology during the last few decades, where rapid advancements in path planning and trajectory optimization approaches have been made possible by the conspicuous enhancements brought, among others, by sampling-based methods and convex optimization strategies. Although they have been investigated from various perspectives in the existing literature, recent developments aimed at integrating robots into social, healthcare, industrial, and educational contexts have attributed greater importance to additional concepts that would allow them to communicate, cooperate, and collaborate with each other, as well as with human beings, in a meaningful and efficient manner. Therefore, in this survey, in addition to a brief overview of some of the essential aspects of motion planning algorithms, a few vital considerations required for assimilating robots into real-world applications, including certain instances of social, urban, and industrial environments, are introduced, followed by a critical discussion of a set of outstanding issues worthy of further investigation and development in future scientific studies.

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“…During manual spraying, the paint is scattered into the surrounding environment, causing chemical pollution and posing a severe risk to the health of the workers [ 1 ]. Spraying operations have been found to have high rates of reportable incidents by the Occupational Safety and Health Administration (OSHA) due to the nature of being an ergonomically difficult job requiring the manipulation and fine control of tools for prolonged periods [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies in autonomous path planning compared the benefits and drawbacks of the existing approaches, most of which are based on tessellated representations in the “Standard Tessellation Language” (STL) format or 3D CAD models [ 4 ]. The authors of [ 1 ] provide a more recent review of actual tool-path-planning approaches for spray painting in which a point cloud is included as a geometrical representation of the model. Another comparison was made in [ 5 ], which includes parametric models and tessellated models as geometry representations but also considers how the model is obtained; in this case, two methods were mentioned: a CAD-based method in which geometries are generated using any existing CAD software and a sensor-based method which includes the use of sensors such as depth cameras, light detection and ranging (Li-DAR), and coordinate-measuring machines.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Trajectory Planning for Spray Painting on Complex Surfaces Based on a Point Cloud Model

Nieto Bastida,

Lin

2023

Sensors

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Using teach pendants or offline programming methods can generate tool paths for robot manipulators to carry out production activities, such as spray painting on objects of different geometries. This task, in which the complexity of painting the surface is one of the main challenges, requires highly skilled operators. In addition, the time spent setting up a robot task can be justified for the mass production of the same workpiece. However, it is inconvenient for low-production and high-variation production lines. In order to overcome these challenges, this study presents an algorithm to autonomously generate robot trajectories for a spray-painting process applied to objects with complex surfaces based on input 3D point cloud data. A predefined spherical mesh wraps the object, organizing the geometrical attributes into a structured data set. Subsequently, the region of interest is extracted and isolated from the model, which serves as the basis for the automatic path-planning operation. A user-friendly graphical user interface (GUI) is developed to define input parameters, visualize the point cloud model and the generated trajectory, simulate paint quality using a color map, and ultimately generate the robot’s code. A 3D sensor is used to localize the pose of the workpiece ahead of the robot and adjust the robot’s trajectory. The efficacy of the proposed approach is validated first by using various workpieces within a simulated environment and second by employing a real robot to execute the motion task.

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Path Planning for Special Robotic Operations

Cited by 7 publications

References 139 publications

End-to-end deep learning-based framework for path planning and collision checking: bin-picking application

End-to-end deep learning-based framework for path planning and collision checking: bin-picking application

Conventional, Heuristic and Learning-Based Robot Motion Planning: Reviewing Frameworks of Current Practical Significance

Autonomous Trajectory Planning for Spray Painting on Complex Surfaces Based on a Point Cloud Model

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