Real-Time Self-Localization of a Mobile Robot by Vision and Motion System

Chiang, Shu‐Yin; Wei, Chi-An; Chen, Ching-Yi

doi:10.1007/s40815-016-0220-y

Cited by 9 publications

(4 citation statements)

References 9 publications

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“…The simulation is performed on the Federation of International Robot-soccer Association (FIRA) 5-versus-5 simulator. 25 The real-world environment is the standard robot soccer game platform. 26 We set some competitors to demonstrate the effect of the proposed method.…”

Section: Methodsmentioning

confidence: 99%

An adaptive cooperation with reinforcement learning for robot soccer games

Meng

Hwang

2020

International Journal of Advanced Robotic Systems

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A strategy system with self-improvement and self-learning abilities for robot soccer system has been developed in this study. This work focuses on the cooperation strategy for the task assignment and develops an adaptive cooperation method for this system. This method was inspired by reinforcement learning (RL) and game theory. The developed system includes two subsystems: the task assignment system and the RL system. The task assignment system assigns one of the four roles, Attacker, Helper, Defender, and Goalkeeper, to each separate robot with the same physical and mechanical conditions to achieve cooperation. The assigned role to robots considers the situation in the game field. Each role has its own behaviors and tasks. The RL helps the Helper and Defender to improve the ability of their policy selection on the real-time confrontation. The RL system can not only learn to figure up how Helper helps its teammates to form an attack or a defense type but also learn to stand a proper defensive strategy. Some experiments on FIRE simulator and standard platform have been demonstrated that the proposed method performs better than the competitors.

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Section: Methodsmentioning

confidence: 99%

An adaptive cooperation with reinforcement learning for robot soccer games

Meng

Hwang

2020

International Journal of Advanced Robotic Systems

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“…Foot robots move at slower speeds than wheel robots but can handle more complex terrains. The most common foot robots are hexapod [12], quadruped, and biped robots [5]. Wheeled robots change direction and move faster, suitable for moving in flat terrain.…”

Section: Robot Movementmentioning

confidence: 99%

“…The mechanical construction and artificial intelligence of robots have developed rapidly with the advancement of science and technology in the past decades [1]. Robots are being increasingly applied, not only in the fields of traditional industry, agriculture, and aquaculture but also in many environments such as medicine [2], medical equipment [3], education [4], home [5], and entertainment [6] where they had been widely used; robots replace manpower to save labor costs for enterprises. Factory automation robots such as handling robots and assembling robots can accelerate productivity and improve product yield.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Obstacle Avoidance for the Mobile System of Service Robots

Tseng¹,

Chen

Kuan

et al. 2020

Wireless Communications and Mobile Computing

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This study implements Fuzzy logic-based obstacle avoidance and human tracking on an omnidirectional mobile system for service robots. The mobile system could be separated and combined with the robot which can be controlled remotely and switched to go forward and avoid obstacles in an indoor environment automatically. The system is able to track and go to the user according to the user’s position. The omnidirectional wheel was adapted in the power system to perform translating and spinning movements. The translating movement enables the robot to avoid obstacles faster and flexibly in paths. With the spinning movement, the robot can quickly find the direction of the object. Finally, the experiments show that the proposed system has good performance in service environments.

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“…where i and j are the ith column and jth row of the grid point, respectively. To eliminate the accumulated distance error, the system model and the estimated coordinates of the robot within a range of 20 cm × 20 cm are compared in four directions (Chiang et al 2016), as shown in Figure 8. Let us assume that the estimated position of the robot is within the black spot.…”

Section: Localization By Image Pattern Match With a System Modelmentioning

confidence: 99%

Localization and obstacle avoidance in soccer competition of humanoid robot by gait and vision system

Chiang

2019

The Knowledge Engineering Review

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In this study, we designed a localization and obstacle avoidance system for humanoid robots in the Federation of International Robot-soccer Association (FIRA) HuroCup united soccer competition event. The localization is implemented by using grid points, gait, and steps to determine the positions of each robot. To increase the localization accuracy and eliminate the accumulated distance errors resulting from step counting, the localization is augmented with image pattern matching using a system model. The system also enables the robot to determine the ball’s position on the field using a color model of the ball. Moreover, to avoid obstacles, the robots calculate the obstacle distance using data extracted from real-time images and determine a suitable direction for movement. With the integration of this accurate self-localization algorithm, ball identification scheme, and obstacle avoidance system, the robot team is capable of accomplishing the necessary tasks for a FIRA soccer game.

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Real-Time Self-Localization of a Mobile Robot by Vision and Motion System

Cited by 9 publications

References 9 publications

An adaptive cooperation with reinforcement learning for robot soccer games

An adaptive cooperation with reinforcement learning for robot soccer games

Fuzzy Obstacle Avoidance for the Mobile System of Service Robots

Localization and obstacle avoidance in soccer competition of humanoid robot by gait and vision system

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