Position control of a mobile robot using reinforcement learning

Farías, Gonzalo; Garcia, Gonzalo; Montenegro, Guelis; Fábregas, Ernesto; Dormido-Canto, S.; Dormido, Sebastián

doi:10.1016/j.ifacol.2020.12.2093

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…However, it has limited computational power to carry out the computational vision (not fitted with a GPU). To take advantage of the potential mentioned above of the Khepera, an embedded system can be integrated that adds the functionality to detect objects using ML models or even Deep Learning (DL), for example [36].…”

Section: Introductionmentioning

confidence: 99%

Mobile Robot Navigation Based on Embedded Computer Vision

et al. 2023

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The current computational advance allows the development of technological solutions using tools, such as mobile robots and programmable electronic systems. We present a design that integrates the Khepera IV mobile robot with an NVIDIA Jetson Xavier NX board. This system executes an algorithm for navigation control based on computer vision and the use of a model for object detection. Among the functionalities that this integration adds to the Khepera IV in generating guided driving are trajectory tracking for safe navigation and the detection of traffic signs for decision-making. We built a robotic platform to test the system in real time. We also compared it with a digital model of the Khepera IV in the CoppeliaSim simulator. The navigation control results show significant improvements over previous works. This is evident in both the maximum navigation speed and the hit rate of the traffic sign detection system. We also analyzed the navigation control, which achieved an average success rate of 93%. The architecture allows testing new control techniques or algorithms based on Python, facilitating future improvements.

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Section: Introductionmentioning

confidence: 99%

Mobile Robot Navigation Based on Embedded Computer Vision

et al. 2023

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“…This is a challenging task due to the complexity of the spherical robot model. The developed model is controlled under different scenarios with several control algorithms implemented by the authors in previous studies, including Villela [24], IPC (integral proportional controller) [25], and reinforcement learning (RL) [26,27]. The experiments undertaken to test the robot model included investigation of position control, path-following and formation control.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Control of a Spherical Robot in the CoppeliaSim Simulator

Montenegro

Chacón

Fábregas

et al. 2022

Sensors

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This article presents the development of a model of a spherical robot that rolls to move and has a single point of support with the surface. The model was developed in the CoppeliaSim simulator, which is a versatile tool for implementing this kind of experience. The model was tested under several scenarios and control goals (i.e., position control, path-following and formation control) with control strategies such as reinforcement learning, and Villela and IPC algorithms. The results of these approaches were compared using performance indexes to analyze the performance of the model under different scenarios. The model and examples with different control scenarios are available online.

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“…This is a challenging task due to the complexity involved in the construction and implementation of control algorithms on a spherical robot. The developed model [23] is controlled in different scenarios with several control algorithms tested by the authors in previous studies [24], including Villela [25], IPC (proportional integral controller) [26], and reinforcement learning (RL) [27,28]. The conducted experiments for testing the mobility of the real robot including the robot design, both electronic and 3D, adjusting the position control, trajectory tracking and comparative analysis between the different scenarios.…”

mentioning

confidence: 99%

Development and Control of a Real Spherical Robot

Schröder

Garcia

Chacón

et al. 2023

Sensors

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This paper presents the design and implementation of a spherical robot with an internal mechanism based on a pendulum. The design is based on significant improvements made, including an electronics upgrade, to a previous robot prototype developed in our laboratory. Such modifications do not significantly impact its corresponding simulation model previously developed in CoppeliaSim, so it can be used with minor modifications. The robot is incorporated into a real test platform designed and built for this purpose. As part of the incorporation of the robot into the platform, software codes are made to detect its position and orientation, using the system SwisTrack, to control its position and speed. This implementation allows successful testing of control algorithms previously developed by the authors for other robots such as Villela, the Integral Proportional Controller, and Reinforcement Learning.

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Position control of a mobile robot using reinforcement learning

Cited by 6 publications

References 13 publications

Mobile Robot Navigation Based on Embedded Computer Vision

Mobile Robot Navigation Based on Embedded Computer Vision

Modeling and Control of a Spherical Robot in the CoppeliaSim Simulator

Development and Control of a Real Spherical Robot

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