Mobile Robot Navigation Using Reinforcement Learning Based on Neural Network with Short Term Memory

Gavrilov, A. V.; Lenskiy, Artem

doi:10.1007/978-3-642-24728-6_28

Cited by 7 publications

(2 citation statements)

References 13 publications

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“…The novelty of their work is the combination of short-term memory and online neural-network learning using the event history stored in this memory. The neural network is trained with a modified error backpropagation algorithm that uses the principle of reward and punishment when interacting with the environment [18]. The robot navigation mechanism is one of the most challenging research topics in mobile robots, which requires the robot to find the right path and travel from its current position to the target position without encountering obstacles.…”

Section: Discussionmentioning

confidence: 99%

A Path Planning Model for Stock Inventory Using a Drone

et al. 2022

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In this study, a model and solution are shown for controlling the inventory of a logistics warehouse in which neither satellite positioning nor IoT solutions can be used. Following a review of the literature on path planning, a model is put forward using a drone that can be moved in all directions and is suitable for imaging and transmission. The proposed model involves three steps. In the first step, a traversal path definition provides an optimal solution, which is pre-processing. This is in line with the structure and capabilities of the warehouse. In the second step, the pre-processed path determines the real-time movement of the drone during processing, including camera movements and image capture. The third step is post-processing, i.e., the processing of images for QR code identification, the interpretation of the QR code, and the examination of matches and discrepancies for inventory control. A key benefit for the users of this model is that the result can be achieved without any external orientation tools, relying solely on its own movement and the organization of a pre-planned route. The proposed model can be effective not only for inventory control, but also for exploring the structure of a warehouse shelving system and determining empty cells.

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Section: Discussionmentioning

confidence: 99%

A Path Planning Model for Stock Inventory Using a Drone

et al. 2022

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“…Among the existing methods in the reinforcement learning approach, we have chosen to use the Q-learning and Qlearning-multi-agent algorithms [11][12][13][14][15] in the case of multirobots.…”

Section: B Intelligent Control Partmentioning

confidence: 99%

Complex safety study of intelligent multi-robot navigation in risk's environment

Bensaci¹,

Zennir²,

Pomorski³

et al. 2017

2017 International Carnahan Conference on Security Technology (ICCST)

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The issue investigated in this paper concerns navigation, survey / control and the complexity associated with a mobile multi-robot coordination and cooperation in a complex environment (robotic analysis laboratory), which is little or no known with significant industrial risks, in the presence of human and machines. This group of mobile robots is mainly used to move chemicals products, which can lead dangerous accidents (toxic, flammable, explosive ...) between the different rooms of the laboratory. The objective of our study is to ensure a good precision in the robots navigation in order to optimize human efforts, reduced error and establishment safety while keeping an eye on robots with good functioning and a desired production. In the literature there are several risk analysis techniques. Among the most used techniques in robotics, the FMEA method (failure modes, effects and criticality analysis). We applied the method FMEA on one robot. Then, the method FTA was chosen to generalize dependability study on all robots. Finally, To manage this level of complexity, a control architecture based on controllers decomposition into a set of elementary behaviors / controllers (obstacles avoidance and collision between robots, attraction to a target, planning ...) was proposed.

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SAC-PER: A Navigation Method Based on Deep Reinforcement Learning Under Uncertain Environments

Wang

Shen

et al. 2023

Lecture Notes in Computer Science

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Mobile Robot Navigation Using Reinforcement Learning Based on Neural Network with Short Term Memory

Cited by 7 publications

References 13 publications

A Path Planning Model for Stock Inventory Using a Drone

A Path Planning Model for Stock Inventory Using a Drone

Complex safety study of intelligent multi-robot navigation in risk's environment

SAC-PER: A Navigation Method Based on Deep Reinforcement Learning Under Uncertain Environments

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