Optimal motion planning by reinforcement learning in autonomous mobile vehicles

Gomez, M.; González, Rubén; Martínez-Marín, T.; Meziat, D.; Sánchez, S. F.

doi:10.1017/s0263574711000452

Cited by 37 publications

(41 citation statements)

References 29 publications

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“…3,8,9 The way in which the goal is reached is as follows: first, a maximum acceleration is performed and before reaching the goal, an inverse maximum acceleration is applied. In all cases, the goal is reached with a null velocity and no oscillations.…”

Section: Problems In Control Based On Cell Mappingmentioning

confidence: 99%

Introducing MultiScale technique with CACM-RL

Mariano

Tomas

Sebastian

2017

International Journal of Advanced Robotic Systems

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Control Adjoining Cell Mapping and Reinforcement Learning (CACM-RL) is a promising technique used to implement controllers. However, it needs many resources so that it can be only applied to simple problems. The contribution of this work is to describe MultiScale approach in order to be used together with CACM-RL technique to overcome its limitations. The main challenge is to verify and validate its efficiency in real-time and in resource-limited systems. MultiScale approach is truly useful when different levels of resolution are needed in the state space, regardless of the number of dimensions. In this way, a set of different regions inside the state space where each region has a specific optimal policy (also different resolutions) is defined. The results described in this article show the feasibility to run MultiScale in real time and find the minimum number of policies to solve the optimal control problem in an automatic way. In the considered test cases, a significant reduction in the total number of cells used is achieved when using MultiScale.

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Section: Problems In Control Based On Cell Mappingmentioning

confidence: 99%

Introducing MultiScale technique with CACM-RL

Mariano

Tomas

Sebastian

2017

International Journal of Advanced Robotic Systems

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“…Designing controllers based on the cell mapping techniques [12][13][14][15][16] results in an efficient optimal control method for nonlinear systems.Cell-to-cell mapping methods are based on a discretization of the state variables of the system, defining a partition of the state space into cells. A cell-to-cell mapping can be derived from the dynamic evolution of the system.…”

Section: Cacm-rlmentioning

confidence: 99%

“…The new algorithm proposed by the authors in [15],CACM-RL, combines the cellmapping techniquesand the reinforcement learning approaches in order to conceive a single efficient optimal control algorithm.…”

Section: Cacm-rlmentioning

confidence: 99%

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Optimal Control Based on CACM-RL in a Two-Wheeled Inverted Pendulum

Gomez

Arribas

Sánchez

2012

International Journal of Advanced Robotic Systems

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This work aims to present a new optimal control scheme based on the CACM-RL technique applied tounstable systems such as a Two-Wheeled Inverted Pendulum (TWIP).The main challenge in this work is to verify and validate the good behaviour of CACM-RL in this kind of system. Learning while maintaining the equilibrium is a complex task. It is easy in stable platforms because the system never reaches an unstable state, but in unstable systems it is very difficult. The study also investigates implementing CACM-RL to coexist with a classic control solution. The results show that the proposed method works perfectly in unstable systems, providing better results than a PID controller.

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“…When building this kind of platform, we need to consider the following requirements [12][13][14][15][16][17]:…”

mentioning

confidence: 99%

Design and Application of an Online English Self-learning Platform

Liu

2017

Int. J. Emerg. Technol. Learn.

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Abstract-This paper discusses the English learning methods and identifies the problems in English teaching and self-learning in China, and then based on the VB platform, it proposes building a platform to facilitate communication between teachers, learners and other participants. At the same time, it adopts the object-oriented development approach to analyze the construction method for the online English self-learning platform and uses UML language to design this platform. At last, this paper uses general testing methods to test the interfaces, functions, performances and security of the VB-based online English selflearning platform and analyzes the performances and deficiencies of the platform based on test results. Keywords-online English; self-learning; B/S; system development IntroductionInterest plays an essential role in English self-learning. Under the guidance of interest, students will take initiatives to search and learn English knowledge. Stimulated by strong interest, a student's nervous system will be very active, and their attention, active thinking and memory will be significantly improved, and as a result, they can receive information well and learn things better [1][2][3]. In order to achieve this purpose, this paper designs an online English self-learning platform, and discusses its application in detail.English learning requires students to take the initiative to learn and have a strong interest in this language. The desire for knowledge can significantly improve students' learning efficiency. If students are aware of this and intentionally carry out such self-learning activities, they will achieve better results [4][5][6]. Under the selflearning model, students will have a more positive understanding of learning and develop a strong passion for knowledge, and in the process, their learning abilities and skills will also improve [7]. Whenever encountering any problem, students will tend to think by themselves rather than learn passively under the teacher's supervision under the traditional model. According to practical experience, this model can significantly increase students' interest and prevent them from getting tired of learning under the traditional learning model. Therefore, it is necessary to study the self-learning model. At present, there are many associated researches, but most of them focus on 4

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Optimal motion planning by reinforcement learning in autonomous mobile vehicles

Cited by 37 publications

References 29 publications

Introducing MultiScale technique with CACM-RL

Introducing MultiScale technique with CACM-RL

Optimal Control Based on CACM-RL in a Two-Wheeled Inverted Pendulum

Design and Application of an Online English Self-learning Platform

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