Experimental Assessment of Deep Reinforcement Learning for Robot Obstacle Avoidance: A LPV Control Perspective

Incremona, Gian Paolo; Sacchi, Nikolas; Sangiovanni, Bianca; Ferrara, Antonella

doi:10.1016/j.ifacol.2021.08.586

Cited by 6 publications

(2 citation statements)

References 15 publications

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“…When it comes to knowledge-based systems, artificial neural networks can be well adapted in an appropriate form. Reference 7 proposed that the experimental evaluation of robot obstacle avoidance using deep reinforcement learning, relying on the equivalent linear parameter change state space representation of the system, and according to the measurement of the distance between the robot and the obstacle, activate two operation modes, one based on joint position and speed, and the other based only on speed input. Therefore, when the obstacle is close to the robot, a switching mechanism is introduced to enable the Deep Reinforcement Learning (DRL) algorithm, thus generating a self configuring architecture to deal with objects moving randomly in the workspace.…”

Section: Review Of Referencesmentioning

confidence: 99%

Design of intelligent controller for obstacle avoidance and navigation of electric patrol mobile robot based on PLC

Liu,

Li,

et al. 2024

Sci Rep

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Currently, the obstacle avoidance control of patrol robots based on intelligent vision lacks professional controller module assistance. Therefore, this paper proposes a design method of intelligent controller for obstacle avoidance and navigation of electrical inspection mobile robot based on PLC control. The controller designs a laser range finder to determine the required position of electrical patrol inspection. Use PLC as the core controller, and combine sensors, actuators, communication module and PLC selection module in the process of hardware design to achieve autonomous navigation and obstacle avoidance functions of the robot. Then design the software including the PLC compiler system and the virtual machine module. Based on the above steps, design the control module of obstacle avoidance navigation, which realizes the key link of robot autonomous navigation. The test results show that the controller can successfully avoid obstacles, improve the efficiency and quality of inspection, and achieve accurate and fast obstacle avoidance navigation for the electrical inspection mobile robot.

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Section: Review Of Referencesmentioning

confidence: 99%

Design of intelligent controller for obstacle avoidance and navigation of electric patrol mobile robot based on PLC

Liu,

Li,

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…More specifically, a switching mechanism is embedded within a dual architecture, where the mode is determined based on the distance between the robot and the obstacles. The two modes may differ from each other in terms of, e.g., whether or not the joint positions are directly controlled, whereby the RL planner takes control when obstacles are perceived to have become too close to the robot [109].…”

Section: Reinforcement Learningmentioning

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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A review of recent trend in motion planning of industrial robots

Tamizi

Yaghoubi

Najjaran

2023

Int J Intell Robot Appl

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Experimental Assessment of Deep Reinforcement Learning for Robot Obstacle Avoidance: A LPV Control Perspective

Cited by 6 publications

References 15 publications

Design of intelligent controller for obstacle avoidance and navigation of electric patrol mobile robot based on PLC

Design of intelligent controller for obstacle avoidance and navigation of electric patrol mobile robot based on PLC

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

A review of recent trend in motion planning of industrial robots

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