A review of recent trend in motion planning of industrial robots

Tamizi, Mehran Ghafarian; Yaghoubi, Marjan; Najjaran, Homayoun

doi:10.1007/s41315-023-00274-2

Cited by 28 publications

(23 citation statements)

References 149 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]. Classical path-planning approaches encompass a wide range of techniques, including artificial potential field [6], bio-inspired heuristic methods [7], and sampling-based path planners [8].…”

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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“…Nevertheless, they were still the foremost category of optimization techniques utilized in motion planning at the time of the widespread deployment of Convolutional Neural Network (CNN)s, which have dominated the field since the year 2015, and more conspicuously, since 2020 [5,125]. This will be discussed in more detail in Section 3.5.…”

Section: Bio-inspired Algorithmsmentioning

confidence: 99%

“…Nevertheless, preparing suitable datasets enabling the network to achieve this is typically not straightforward. This may be alleviated by decreasing the number of samples involved in training through active learning, where only certain samples chosen by the DL network itself are supplied in each round of training [5,97,98].…”

Section: Deep Learningmentioning

confidence: 99%

“…This undermines their practical usefulness. Nevertheless, an efficient reward function may help reduce the amount of data required [5,104].…”

Section: Reinforcement Learningmentioning

confidence: 99%

“…A schematic representation of the role of motion planning within the workflow of AI-based systems is shown in Figure 1, together with more specific illustrations of its place in the structure governing the performance of robotic systems and self-driving cars. The figure was inspired by the definitions and representations provided in [1,5]. Motion planning in cluttered environments is important in various applications relying on autonomous systems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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A Survey of Trajectory Planning Algorithms for Off-Road Uncrewed Ground Vehicles

Gargano,

von Ellenrieder,

Vivolo

2024

Lecture Notes in Computer Science

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A review of recent trend in motion planning of industrial robots

Cited by 28 publications

References 149 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

A Survey of Trajectory Planning Algorithms for Off-Road Uncrewed Ground Vehicles

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