Robot obstacle avoidance system using deep reinforcement learning

Zhu, Xiaojun; Liang, Yinghao; Wang, Xueqian; Ren, Biying

doi:10.1108/ir-06-2021-0127

Cited by 13 publications

(8 citation statements)

References 22 publications

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“…Meyesa et al [21] present an approach based on RL and Q-learning enabling an agent to control a six-axis industrial robot to play the wire loop game using a camera. Zhu et al [22] propose a novel collision avoidance framework using a depth camera that allows robots to work alongside human operators in unstructured and complex environments. Of note, the above works rely on sensors to perceive the environment and act accordingly.…”

Section: Related Workmentioning

confidence: 99%

Deep-Reinforcement-Learning-based Path Planning for Industrial Robots using Distance Sensors as Observation

Bhuiyan¹,

Kästner²,

Hu³

et al. 2023

Preprint

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Industrial robots are widely used in various manufacturing environments due to their efficiency in doing repetitive tasks such as assembly or welding. A common problem for these applications is to reach a destination without colliding with obstacles or other robot arms. Most commonly used sampling-based path planning approaches such as RRT require long computation times, especially in complex environments. Furthermore, the environment in which they are employed needs to be known beforehand. When utilizing the approaches in new environments, a tedious engineering effort in setting hyperparameters needs to be conducted, which is time-and cost-intensive. On the other hand, Deep Reinforcement Learning has shown remarkable results in dealing with unknown environments, generalizing new problem instances, and solving motion planning problems efficiently. On that account, this paper proposes a Deep-Reinforcement-Learning-based motion planner for robotic manipulators. We evaluated our model against stateof-the-art sampling-based planners in several experiments. The results show the superiority of our planner in terms of path length and execution time.

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Section: Related Workmentioning

confidence: 99%

Deep-Reinforcement-Learning-based Path Planning for Industrial Robots using Distance Sensors as Observation

Bhuiyan¹,

Kästner²,

Hu³

et al. 2023

Preprint

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“…Based on the idea described above which is intensely motivating the current article to pursue and work out a state-of-the-art smart robot. Accordingly, in Zhu et al (2022), a context-gated convolution is adopted to build an end-to-end learning framework, which enables convolutional layers that dynamically capture representative local patterns and composes local features of interest under the guidance of global context.…”

Section: Related Research Studies and Contributionsmentioning

confidence: 99%

“…Nevertheless, the integration of ML in developing the smart robot system is an absolutely positive way for the relevant research field. As such ML issue addressed and carried out demonstrated in Asavasirikulkij et al (2021), Zhu et al (2022) where has explicated the application of ML in the development of robotic system.…”

Section: Introductionmentioning

confidence: 99%

The implementation and performance evaluation for a smart robot with edge computing algorithms

Chen

Huang

2022

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Purpose Apart from, the smart edge computing (EC) robot (SECR) provides the tools to manage Internet of things (IoT) services in the edge landscape by means of real-world test-bed designed in ECR. Eventually, based on the results from two experiments held in little constrained condition, such as the maximum data size is 2GB, the performance of the proposed techniques demonstrate the effectiveness, scalability and performance efficiency of the proposed IoT model. Design/methodology/approach Certainly, the proposed SECR is trying primarily to take over other traditional static robots in a centralized or distributed cloud environment. One aspect of representation of the proposed edge computing algorithms is due to challenge to slow down the consumption of time which happened in an artificial intelligence (AI) robot system. Thus, the developed SECR trained by tiny machine learning (TinyML) techniques to develop a decentralized and dynamic software environment. Findings Specifically, the waste time of SECR has actually slowed down when it is embedded with Edge Computing devices in the demonstration of data transmission within different paths. The TinyML is applied to train with image data sets for generating a framework running in the SECR for the recognition which has also proved with a second complete experiment. Originality/value The work presented in this paper is the first research effort, and which is focusing on resource allocation and dynamic path selection for edge computing. The developed platform using a decoupled resource management model that manages the allocation of micro node resources independent of the service provisioning performed at the cloud and manager nodes. Besides, the algorithm of the edge computing management is established with different path and pass large data to cloud and receive it. In this work which considered the SECR framework is able to perform the same function as that supports to the multi-dimensional scaling (MDS).

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“…Therefore, the design and verification of automatic path planning for obstacle avoidance of intelligent substation inspection robot are proposed. Considering the authenticity and stability of the final test results, the inspection robot in the real intelligent substation is selected as the actual measurement target [6]. This time, intelligent information technology and 3D control technology are integrated to improve and optimize the built-in structure of the patrol robot, gradually forming a more flexible and changeable obstacle avoidance path automatic planning structure, and creating a stable and reliable self path planning environment [7].…”

Section: Introductionmentioning

confidence: 99%

Research on automatic path planning for obstacle avoidance of intelligent substation patrol robot

Lu,

Ning,

2023

International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023)

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Most of the current automatic planning and execution codes for the obstacle avoidance path of the patrol robot in the substation are single structure, and the planning scope is limited, which leads to the increase of the optimal time consumption of the automatic planning. Therefore, the research on the automatic planning of the obstacle avoidance path of the patrol robot in the intelligent substation is proposed. According to the actual planning requirements and standards, the basic data collection of the robot is carried out, and the obstacle avoidance operator is set. In the form of multiobjective, expand the actual planning scope, design the robot multi-objective obstacle avoidance path code, and based on this, calculate the fitness function, design an adaptive three-dimensional obstacle avoidance path automatic planning model, and use depth grid modification to achieve automatic path planning processing. The final test results show that after three stages and five independent unit areas of test comparison, the TR20 robot selected in the G intelligent substation has relatively high completion rate for the test task, and better control the optimal time consumption of automatic obstacle avoidance path planning to less than 0.5s, indicating that this kind of planning scheme will be more targeted and stable. The patrol robot can also avoid the area where obstacles appear faster and more timely, and design the shortest path, which has practical application value.

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Robot obstacle avoidance system using deep reinforcement learning

Cited by 13 publications

References 22 publications

Deep-Reinforcement-Learning-based Path Planning for Industrial Robots using Distance Sensors as Observation

Deep-Reinforcement-Learning-based Path Planning for Industrial Robots using Distance Sensors as Observation

The implementation and performance evaluation for a smart robot with edge computing algorithms

Research on automatic path planning for obstacle avoidance of intelligent substation patrol robot

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