Vision-based obstacle avoidance system with fuzzy logic for humanoid robots

2016 ◽  
Vol 32 ◽  
Author(s):  
Shu-Yin Chiang
Keyword(s):  
Image Processing ◽  
Fuzzy Logic ◽  
Obstacle Avoidance ◽  
Humanoid Robot ◽  
Vision System ◽  
Humanoid Robots ◽  
Robot Motion ◽  
Connected Component ◽  
Open Platform ◽  
Connected Component Labeling

AbstractThis study presents the algorithm for a humanoid robot to accomplish an obstacle run in the FIRA HuroCup competition. It includes the integration of image processing and robot motion. DARwIn-OP (Dynamic Anthropomorphic Robot with Intelligence–Open Platform) was used as the humanoid robot, and it is equipped with a webcam as a vision system to obtain an image of what is in front of the robot. Image processing skills such as erosion, dilation, and eight-connected component labeling are applied to reduce image noise. Moreover, we use navigation grids with filters to avoid the obstacles. Fuzzy logic rules are used to implement the robot’s motion, allowing a humanoid robot to access any routes using obstacle avoidance to perform the tasks in the obstacle-run event.

Application of Camera Image Processing to Control of Humanoid Robot Motion

2014 ◽  
Vol 474 ◽  
pp. 179-185
Author(s):  
Rastislav Ďuriš
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Humanoid Robot ◽  
Humanoid Robots ◽  
Robot Motion ◽  
Potential Applications ◽  
Processing Techniques ◽  
And Control ◽  
Detection And Localization ◽  
Wide Potential

The wide potential applications of humanoid robots require that the robots can move in general environment, overcome various obstacles, detect predefined objects and control of its motion according to all these parameters. The goal of this paper is address the problem of implementation of computer vision to motion control of humanoid robot. We focus on using of computer vision and image processing techniques, based on which the robot can detect and recognize a predefined color object in a captured image. An algorithm to detection and localization of objects is described. The results obtained from image processing are used in an algorithm for controlling of the robot movement.

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

2019 ◽  
Vol 34 ◽  
Author(s):  
Shu-Yin Chiang ◽  
Jia-Huei Lu
Keyword(s):  
Obstacle Avoidance ◽  
Humanoid Robot ◽  
Vision System ◽  
Humanoid Robots ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
Step Counting ◽  
Grid Points ◽  
Using Data ◽  
Image Pattern

Abstract 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.

The Development of Supervised Motion Learning and Vision System for Humanoid Robot

2019 ◽  
Vol 886 ◽  
pp. 188-193 ◽  
Author(s):  
Ssu Ting Lin ◽  
Jun Hu ◽  
Chia Hung Shih ◽  
Chiou Jye Huang ◽  
Ping Huan Kuo
Keyword(s):  
Humanoid Robot ◽  
Physical Mechanism ◽  
Learning Algorithm ◽  
Vision System ◽  
Humanoid Robots ◽  
Learning System ◽  
Automatic Learning ◽  
Swarm Optimization ◽  
Motion Learning ◽  
Important Research Topic

With the development of the concept of Industry 4.0, research relating to robots is being paid more and more attention, among which the humanoid robot is a very important research topic. The humanoid robot is a robot with a bipedal mechanism. Due to the physical mechanism, humanoid robots can maneuver more easily in complex terrains, such as going up and down the stairs. However, humanoid robots often fall from imbalance. Whether or not the robot can stand up on its own after a fall is a key research issue. However, the often used method of hand tuning to allow robots to stand on its own is very inefficient. In order to solve the above problems, this paper proposes an automatic learning system based on Particle Swarm Optimization (PSO). This system allows the robot to learn how to achieve the motion of rebalancing after a fall. To allow the robot to have the capability of object recognition, this paper also applies the Convolutional Neural Network (CNN) to let the robot perform image recognition and successfully distinguish between 10 types of objects. The effectiveness and feasibility of the motion learning algorithm and the CNN based image classification for vision system proposed in this paper has been confirmed in the experimental results.

Walking Control of Humanoid Robots on Uneven Ground Using Fuzzy Algorithm

2019 ◽  
pp. 352-361
Author(s):  
Saeed Abdolshah ◽  
Mohammad Abdolshah ◽  
Majid Abdolshah ◽  
S. Vahid Hashemi
Keyword(s):  
Output Signal ◽  
Humanoid Robot ◽  
Humanoid Robots ◽  
Moment Of Inertia ◽  
Robot Motion ◽  
Human Being ◽  
Fuzzy Algorithm ◽  
Walking Control ◽  
The Moment ◽  
Flat Ground

Walking control of humanoid robots is a challenging issue. In this chapter, a method for modeling humanoid robots is presented considering the human being indices such as DOFs, mass and the moment of inertia of the segments. In the next step, a walking pattern on the flat ground is generated and the robot motion is simulated in the MSC. Visual Nastran 4D™ software. ZMP trajectory of the simulated humanoid robot in walking cycle has been obtained. An uneven ground is generated in the software, where the robot falls down during the motion. A fuzzy algorithm is employed to balance the robot; input is defined as the differences between the projections of ZMP in flat and uneven ground and output is a compensative signal to make the robot follow the flat ground ZMP pattern to refuse the robot falling. Output signal is distributed in different joints to make faster and more effective compensation. Although the type of uneven ground can be important, but the robot could successfully pass the designed uneven ground in MSC.Visual Nastran 4D.

FPGA-Based Object Detection and Motion Tracking in Micro- and Nanorobotics

2014 ◽  
pp. 251-261
Author(s):  
Claas Diederichs ◽  
Sergej Fatikow
Keyword(s):  
Image Processing ◽  
Object Detection ◽  
High Speed ◽  
Motion Tracking ◽  
Principal Component ◽  
Fpga Implementation ◽  
Connected Components ◽  
Connected Component ◽  
Connected Component Labeling ◽  
Labeling Approach

Object-detection and classification is a key task in micro- and nanohandling. The microscopic imaging is often the only available sensing technique to detect information about the positions and orientations of objects. FPGA-based image processing is superior to state of the art PC-based image processing in terms of achievable update rate, latency and jitter. A connected component labeling algorithm is presented and analyzed for its high speed object detection and classification feasibility. The features of connected components are discussed and analyzed for their feasibility with a single-pass connected component labeling approach, focused on principal component analysis-based features. It is shown that an FPGA implementation of the algorithm can be used for high-speed tool tracking as well as object classification inside optical microscopes. Furthermore, it is shown that an FPGA implementation of the algorithm can be used to detect and classify carbon-nanotubes (CNTs) during image acquisition in a scanning electron microscope, allowing fast object detection before the whole image is captured.

Connected component labeling algorithm in streaming image processing for use in FPGAs

2021 ◽  
Author(s):  
Alexey Bakumenko ◽  
Valentin Bakhchevnikov ◽  
Vladimir Derkachev ◽  
Andrey Kovalev ◽  
Vladimir Lobach ◽  
...  
Keyword(s):  
Image Processing ◽  
Connected Component ◽  
Connected Component Labeling ◽  
Labeling Algorithm

A Study on Fuzzy Control of Humanoid Soccer Robot EFuRIO for Vision Control System and Walking Movement

2012 ◽  
Vol 16 (3) ◽  
pp. 444-452
Author(s):  
Indra Adji Sulistijono ◽  
◽  
Son Kuswadi ◽  
One Setiaji ◽  
Inzar Salfikar ◽  
...  
Keyword(s):  
Fuzzy Logic Control ◽  
Humanoid Robot ◽  
Vision System ◽  
Humanoid Robots ◽  
Soccer Robot ◽  
Balance Condition ◽  
Logic Control ◽  
Control Scheme ◽  
Vision Control ◽  
The Stability

Instability is one of the major defects in humanoid robots. Recently, various methods on the stability and reliability of humanoid robots have been studied actively. We propose a new fuzzy-logic control scheme for vision systems that would enable a robot to search for and to kick a ball towards an opponent goal. In this paper, a stabilization algorithm is proposed using the balance condition of the robot, which is measured using accelerometer sensors during standing and walking, and turning movement are estimated from these data. From this information the robot selects the appropriate motion pattern effectively. In order to generate the appropriate reaction in various body of robot situations, a fuzzy algorithm is applied in finding the appropriate angle of the joint from the vision system. The performance of the proposed algorithm is verified by searching for a ball, walking, turning tap and ball kicking movement experiments using an 18-DOF humanoid robot, called EFuRIO.

VISION-BASED OBSTACLE AVOIDANCE NAVIGATION WITH AUTONOMOUS HUMANOID ROBOTS FOR STRUCTURED COMPETITION PROBLEMS

2013 ◽  
Vol 10 (03) ◽  
pp. 1350021 ◽  
Author(s):  
CHUNG-HSIEN KUO ◽  
HUNG-CHYUN CHOU ◽  
SHOU-WEI CHI ◽  
YU-DE LIEN
Keyword(s):  
Obstacle Avoidance ◽  
Humanoid Robot ◽  
Ground Truth ◽  
Humanoid Robots ◽  
Visual Localization ◽  
Adult Size ◽  
Map Construction ◽  
Rule Approach ◽  
High Level ◽  
Autonomous Humanoid

Biped humanoid robots have been developed to successfully perform human-like locomotion. Based on the use of well-developed locomotion control systems, humanoid robots are further expected to achieve high-level intelligence, such as vision-based obstacle avoidance navigation. To provide standard obstacle avoidance navigation problems for autonomous humanoid robot researches, the HuroCup League of Federation of International Robot-Soccer Association (FIRA) and the RoboCup Humanoid League defined the conditions and rules in competitions to evaluate the performance. In this paper, the vision-based obstacle avoidance navigation approaches for humanoid robots were proposed in terms of combining the techniques of visual localization, obstacle map construction and artificial potential field (APF)-based reactive navigations. Moreover, a small-size humanoid robot (HuroEvolutionJR) and an adult-size humanoid robot (HuroEvolutionAD) were used to evaluate the performance of the proposed obstacle avoidance navigation approach. The navigation performance was evaluated with the distance of ground truth trajectory collected from a motion capture system. Finally, the experiment results demonstrated the effectiveness of using vision-based localization and obstacle map construction approaches. Moreover, the APF-based navigation approach was capable of achieving smaller trajectory distance when compared to conventional just-avoiding-nearest-obstacle-rule approach.

FPGA-Based Object Detection and Motion Tracking in Micro- and Nanorobotics

2013 ◽  
Vol 3 (1) ◽  
pp. 27-37 ◽  
Author(s):  
Claas Diederichs ◽  
Sergej Fatikow
Keyword(s):  
Image Processing ◽  
Object Detection ◽  
High Speed ◽  
Motion Tracking ◽  
Principal Component ◽  
Fpga Implementation ◽  
Connected Components ◽  
Connected Component ◽  
Connected Component Labeling ◽  
Labeling Approach

Object-detection and classification is a key task in micro- and nanohandling. The microscopic imaging is often the only available sensing technique to detect information about the positions and orientations of objects. FPGA-based image processing is superior to state of the art PC-based image processing in terms of achievable update rate, latency and jitter. A connected component labeling algorithm is presented and analyzed for its high speed object detection and classification feasibility. The features of connected components are discussed and analyzed for their feasibility with a single-pass connected component labeling approach, focused on principal component analysis-based features. It is shown that an FPGA implementation of the algorithm can be used for high-speed tool tracking as well as object classification inside optical microscopes. Furthermore, it is shown that an FPGA implementation of the algorithm can be used to detect and classify carbon-nanotubes (CNTs) during image acquisition in a scanning electron microscope, allowing fast object detection before the whole image is captured.

Walking Control of Humanoid Robots on Uneven Ground Using Fuzzy Algorithm

2017 ◽  
pp. 304-313 ◽  
Author(s):  
Saeed Abdolshah ◽  
Mohammad Abdolshah ◽  
Majid Abdolshah ◽  
S. Vahid Hashemi
Keyword(s):  
Output Signal ◽  
Humanoid Robot ◽  
Humanoid Robots ◽  
Moment Of Inertia ◽  
Robot Motion ◽  
Human Being ◽  
Fuzzy Algorithm ◽  
Walking Control ◽  
The Moment ◽  
Flat Ground

Walking control of humanoid robots is a challenging issue. In this chapter, a method for modeling humanoid robots is presented considering the human being indices such as DOFs, mass and the moment of inertia of the segments. In the next step, a walking pattern on the flat ground is generated and the robot motion is simulated in the MSC. Visual Nastran 4D™ software. ZMP trajectory of the simulated humanoid robot in walking cycle has been obtained. An uneven ground is generated in the software, where the robot falls down during the motion. A fuzzy algorithm is employed to balance the robot; input is defined as the differences between the projections of ZMP in flat and uneven ground and output is a compensative signal to make the robot follow the flat ground ZMP pattern to refuse the robot falling. Output signal is distributed in different joints to make faster and more effective compensation. Although the type of uneven ground can be important, but the robot could successfully pass the designed uneven ground in MSC.Visual Nastran 4D.

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