Robot In Real Environment Research Articles

Development of Magnetic-Based Navigation by Constructing Maps Using Machine Learning for Autonomous Mobile Robots in Real Environments.

Localization is fundamental to enable the use of autonomous mobile robots. In this work, we use magnetic-based localization. As Earth’s geomagnetic field is stable in time and is not affected by nonmagnetic materials, such as a large number of people in the robot’s surroundings, magnetic-based localization is ideal for service robotics in supermarkets, hotels, etc. A common approach for magnetic-based localization is to first create a magnetic map of the environment where the robot will be deployed. For this, magnetic samples acquired a priori are used. To generate this map, the collected data is interpolated by training a Gaussian Process Regression model. Gaussian processes are nonparametric, data-drive models, where the most important design choice is the selection of an adequate kernel function. These models are flexible and generate mean predictions as well as the confidence of those predictions, making them ideal for their use in probabilistic approaches. However, their computational and memory cost scales poorly when large datasets are used for training, making their use in large-scale environments challenging. The purpose of this study is to: (i) enable magnetic-based localization on large-scale environments by using a sparse representation of Gaussian processes, (ii) test the effect of several kernel functions on robot localization, and (iii) evaluate the accuracy of the approach experimentally on different large-scale environments.

Biomimetic soft micro-swimmers: from actuation mechanisms to applications.

Underwater robot designs inspired by the behavior and morphological characteristics of aquatic animals can provide reinforced mobility and energy efficiency. In the past two decades, the emerging materials science and integrated circuit technology have been combined and applied to various types of bionic soft underwater miniaturized robots by researchers around the world. Further, the potential applications of biomimetic soft micro-swimmers in the biological and medical fields have been explored. Here, this paper reviews the development of biomimetic soft tiny swimmers, which are designed based on a variety of intelligent materials and control strategies. This review focuses on the various actuation mechanisms of soft tiny swimmers reported in the past two decades and classifies these robots into four categories: fish-like, snake-like, jellyfish-like and microbial-inspired ones. Besides, this review considers the practical challenges faced by actuation mechanisms of each type of robot, and summarizes and prospects how these challenges affect the potential applications of robots in real environments.

Wheelchair robot navigation in different weather conditions using deep learning and evolved neural controller

PurposeWheelchair robot navigation in different weather conditions using single camera is still a challenging task. The purpose of this study is to develop an autonomous wheelchair robot navigation method in different weather conditions, with single camera vision to assist physically disabled people.Design/methodology/approachA road detection method, called dimensionality reduction deep belief neural network (DRDBNN), is proposed for drivable road detection. Due to the dimensionality reduction ability of the DRDBNN, it detects the drivable road area in a short time for controlling the robot in real-time. A feed-forward neural network is used to control the robot for the boundary following navigation using evolved neural controller (ENC). The robot detects road junction area and navigates throughout the road, except in road junction, using calibrated camera and ENC. In road junction, it takes turning decision using Google Maps data, thus reaching the final destination.FindingsThe developed method is tested on a wheelchair robot in real environments. Navigation in real environments indicates that the wheelchair robot moves safely from source to destination by following road boundary. The navigation performance in different weather conditions of the developed method has been demonstrated by the experiments.Originality/valueThe wheelchair robot can navigate in different weather conditions. The detection process is faster than that of the previous DBNN method. The proposed ENC uses only distance information from the detected road area and controls the robot for boundary following navigation. In addition, it uses Google Maps data for taking turning decision and navigation in road junctions.

Developing a Humanoid Robot Platform

This paper is focused on developing a platform that helps researchers to create verify and implement their machine learning algorithms to a humanoid robot in real environment. The presented platform is durable, easy to fix, upgrade, fast to assemble and cheap. Also, using this platform we present an approach that solves a humanoid balancing problem, which uses only fully connected neural network as a basic idea for real time balancing. The method consists of 3 main conditions: 1) using different types of sensors detect the current position of the body and generate the input information for the neural network, 2) using fully connected neural network produce the correct output, 3) using servomotors make movements that will change the current position to the new one. During field test the humanoid robot can balance on the moving platform that tilts up to 10 degrees to any direction. Finally, we have shown that using our platform we can do research and compare different neural networks in similar conditions which can be important for the researchers to do analyses in machine learning and robotics

A use case of an adaptive cognitive architecture for the operation of humanoid robots in real environments

Future trends in robotics call for robots that can work, interact and collaborate with humans. Developing these kind of robots requires the development of intelligent behaviours. As a minimum standard for behaviours to be considered as intelligent, it is required at least to present the ability to learn skills, represent skill’s knowledge and adapt and generate new skills. In this work, a cognitive framework is proposed for learning and adapting models of robot skills knowledge. The proposed framework is meant to allow for an operator to teach and demonstrate the robot the motion of a task skill it must reproduce; to build a knowledge base of the learned skills knowledge allowing for its storage, classification and retrieval; to adapt and generate new models of a skill for compliance with the current task constraints. This framework has been implemented in the humanoid robot HOAP-3 and experimental results show the applicability of the approach.

Special Issue on Real World Robot Challenge in Tsukuba – Autonomous Technology for Coexistence with Human Beings –

This issue is the third in a series on Real World Robot Challenge in Tsukuba. The Tsukuba Challenge has contributed much in establishing autonomous mobile robot control technology on outdoor walkways where robots must mingle with pedestrians and cyclists – not all of whom may be familiar with such robots. The rain on the day of the final run for the 2015 Tsukuba Challenge taught us valuable lessons in navigating robots in real environments. Since the 2013 Tsukuba Challenge, a new task was introduced in the second stage. This task consists of searching for human targets – a technological challenge for developing robots that are both mobile and useful. Our objective here is to share advanced control technology refined through experiments in the real-world environment of the Tsukuba Challenge. The Tsukuba Challenge is also providing a forum for technological education for university students studying robotics engineering and for technical exchange through open experiments. In this issue, we are pleased to present the control technology that this exchange has brought about. We would like to express our deep gratitude to the authors contributing to this issue and to the article reviewers who have helped make this all possible.

A PERFORMANCE PREDICTIVE MODEL FOR STEADY SWIMMING OF A FISH ROBOT

Swimming performance is one of the primary concerns and applications to the underwater robots, such as thrust force relating to swimming velocity. As fish's swimming involves the kinematics of its own body and the hydrodynamic interaction with the surrounding fluid, it is difficult to formulate a precise mathematical model by purely analytical approaches. In order to avoid tedious parameter studies in evaluating its performance, this paper proposes a semi-empirical method to model the steady-state swimming performance of a BCF (body and/or caudal fin) biomimetic robotic robot. By using a dimensional analysis method, the semi-empirical model for predicting the thrust force generated by a BCF-oscillating swimming mode is derived. Thereafter, the swimming velocity prediction model is established based on the predictive thrust model together with the use of fundamental theory on drag force and the regression analysis on the experimental data. The model shows a reasonable prediction capability as the resultant predicted results are in good agreement with experiment data. Therefore, the proposed modeling method can be used for a quick prediction of the swimming performance in terms of thrust and velocity. The proposed methodology can be extended to other types of fish robots in real environment, by including changes to relevant parameters.

2P1-G07 限定的通信手法と強化学習を用いた移動ロボット群のため協調行動獲得方法

When a robot moves, there should be a lot of information processing underlying o cope with an uncertain environment, especially in cases where many different mobile robots exist. Sensors such as cameras and laser range finders are needed to recognize the environment. But such accurate devices are expensive. By this reason, a short range telecommunication protocol that is simple and inexpensive is needed. This paper is to study a design of co-operative mobile robots in real environment by proposing a telecommunication system that is simple and cheap but acting sufficiently to maintain co-operative behaviors. The idea is to send a robot's "conviction level" with each other and accelerates to prioritize" experienced robots" to gain rapid congestion resolution. The robot learns this behavior by using the conviction level. Action acquisition experiment was done by using the propsed system. The results show the paper actions were learned through this system.

DESIGN PRINCIPLES FOR DEPENDABLE ROBOTIC ASSISTANTS

A large number of functionalities have been integrated into a single fully autonomous humanoid robot, HERMES. To evaluate the dependability of this extremely complex machine, and its ability to interact with strangers, HERMES was exhibited in a museum, far away from its home laboratory, for more than six months. During this period the robot and its skills were regularly demonstrated to the public by non-expert presenters up to 12 hours per day. Also, HERMES interacted with the visitors, chatted with them in English, French and German, answered questions and performed services as requested by them. Only three major failures occurred during the 6-month period, all of them caused by failures of commercially available modules that could easily be replaced. The key to this success was the dependability that had been originally designed into HERMES. During the design process certain design principles were followed in both hardware and software. These principles are introduced, and some long- and short-term experiments carried out with the real robot in real environments are presented. In fact, by demonstrating HERMES in the museum, at trade fairs and in TV studios — besides our institute environment — we have learned valuable lessons, especially regarding the interaction of a complex robotic assistant with unknown humans. Although we did not quantitatively evaluate the robot's performance or acceptance by the non-expert users, several qualitative results are given in this paper, and many videos highlighting these results can be downloaded from the HERMES homepage.

Evolving mobile robots in simulated and real environments.

The problem of the validity of simulation is particularly relevant for methodologies that use machine learning techniques to develop control systems for autonomous robots, as, for instance, the artificial life approach known as evolutionary robotics. In fact, although it has been demonstrated that training or evolving robots in real environments is possible, the number of trials needed to test the system discourages the use of physical robots during the training period. By evolving neural controllers for a Khepera robot in computer simulations and then transferring the agents obtained to the real environment we show that (a) an accurate model of a particular robot-environment dynamics can be built by sampling the real world through the sensors and the actuators of the robot; (b) the performance gap between the obtained behaviors in simulated and real environments may be significantly reduced by introducing a "conservative" form of noise; (c) if a decrease in performance is observed when the system is transferred to a real environment, successful and robust results can be obtained by continuing the evolutionary process in the real environment for a few generations.

Evolving Mobile Robots in Simulated and Real Environments

The problem of the validity of simulation is particularly relevant for methodologies that use machine learning techniques to develop control systems for autonomous robots, as, for instance, the artificial life approach known as evolutionary robotics. In fact, although it has been demonstrated that training or evolving robots in real environments is possible, the number of trials needed to test the system discourages the use of physical robots during the training period. By evolving neural controllers for a Khepera robot in computer simulations and then transferring the agents obtained to the real environment we show that (a) an accurate model of a particular robot-environment dynamics can be built by sampling the real world through the sensors and the actuators of the robot; (b) the performance gap between the obtained behaviors in simulated and real environments may be significantly reduced by introducing a "conservative" form of noise; (c) if a decrease in performance is observed when the system is transferred to a real environment, successful and robust results can be obtained by continuing the evolutionary process in the real environment for a few generations.