Volume 15, Number 5, 2018
Special Issue on Intelligent Control and Computing in Advanced Robotics (pp.513-602)
In recent years, a large number of relatively advanced and often ready-to-use robotic hardware components and systems have been developed for small-scale use. As these tools are mature, there is now a shift towards advanced applications. These often require automation and demand reliability, efficiency and decisional autonomy. New software tools and algorithms for artificial intelligence (AI) and machine learning (ML) can help here. However, since there are many software-based control approaches for small-scale robotics, it is rather unclear how these can be integrated and which approach may be used as a starting point. Therefore, this paper attempts to shed light on existing approaches with their advantages and disadvantages compared to established requirements. For this purpose, a survey was conducted in the target group. The software categories presented include vendor-provided software, robotic software frameworks (RSF), scientific software and in-house developed software (IHDS). Typical representatives for each category are described in detail, including SmarAct precision tool commander, MathWorks Matlab and national instruments LabVIEW, as well as the robot operating system (ROS). The identified software categories and their representatives are rated for end user satisfaction based on functional and non-functional requirements, recommendations and learning curves. The paper concludes with a recommendation of ROS as a basis for future work.
With the advancing of industrialization and the advent of the information age, intelligent robots play an increasingly important role in intelligent manufacturing, intelligent transportation system, the Internet of things, medical health and intelligent services. Based on working experiences in and reviews on intelligent robot studies both in China and abroad, the authors summarized researches on key and leading technologies related to human-robot collaboration, driverless technology, emotion recognition, brain-computer interface, bionic software robot and cloud platform, big data network, etc. The development trend of intelligent robot was discussed, and reflections on and suggestions to intelligent robot development in China were proposed. The review is not only meant to overview leading technologies of intelligent robot all over the world, but also provide related theories, methods and technical guidance to the technological and industrial development of intelligent robot in China.
This paper proposes a simple solution for the stabilization of a mini-quadcopter carrying a 3DoF (degrees of freedom) manipulator robot in order to enhance its achievable workspace and application profile. Since the motion of the arm induces torques which degrade the stability of the system, in the present work, we consider the stabilization of both subsystems: the quadcopter and the robotic arm. The mathematical model of the system is based on quaternions. Likewise, an attitude control law consisting of a bounded quaternion-based feedback stabilizes the quadcopter to a desired attitude while the arm is evolving. The next stage is the translational dynamics which is simplified for control (nonlinear) design purposes. The aforementioned controllers are based on saturation functions whose stability is explicitly proved in the Lyapunov sense. Finally, experimental results and a statistical study validate the proposed control strategy.
With the development of artificial intelligence and robotics, the study on service robot has made a significant progress in recent years. Service robot is required to perceive users and environment in unstructured domestic environment. Based on the perception, service robot should be capable of understanding the situation and discover service task. So robot can assist humans for home service or health care more accurately and with initiative. Human can focus on the salient things from the mass observation information. Humans are capable of utilizing semantic knowledge to make some plans based on their understanding of the environment. Through intelligent space platform, we are trying to apply this process to service robot. A selective attention guided initiatively semantic cognition algorithm in intelligent space is proposed in this paper. It is specifically designed to provide robots with the cognition needed for performing service tasks. At first, an attention selection model is built based on saliency computing and key area. The area which is highly relevant to service task could be located and referred as focus of attention (FOA). Second, a recognition algorithm for FOA is proposed based on a neural network. Some common objects and user behavior are recognized in this step. At last, a unified semantic knowledge base and corresponding reasoning engine is proposed using recognition result. Related experiments in a real life scenario demonstrated that our approach is able to mimic the recognition process in humans, make robots understand the environment and discover service task based on its own cognition. In this way, service robots can act smarter and achieve better service efficiency in their daily work.
This paper proposes a novel nonlinear energy-based coupling control for an underactuated offshore ship-mounted crane, which guarantees both precise trolley positioning and payload swing suppressing performances under external sea wave disturbance. In addition to having such typical nonlinear underactuated property, as it is well known, an offshore ship-mounted crane also suffers from much unexpected persistent disturbances induced by sea waves or currents, which, essentially different from an overhead crane fixed on land, cause much difficulty in modeling and controller design. Inspired by the desire to achieve appropriate control performance against those challenging factors, in this paper, through carefully analyzing the inherent mechanism of the nonlinear dynamics, we first construct a new composite signal to enhance the coupling behavior of the trolley motion as well as the payload swing in the presence of ship′s roll motion disturbance. Based on which, an energy-based coupling control law is presented to achieve asymptotic stability of the crane control system′s equilibrium point. Without any linearization of the complex nonlinear dynamics, unlike traditional feedback controllers, the proposed control law takes a much simpler structure independent of the system parameters. To support the theoretical derivations and to further verify the actual control performance, Lyapunov-based mathematical analysis as well as numerical simulation/experimental results are carried out, which clarify the feasibility and superior performance of the proposed method over complicated disturbances.
In order to improve the learning ability of robots, we present a reinforcement learning approach with a knowledge base for mapping natural language instructions to executable action sequences. A simulated platform with physical engine is built as interactive environment. Based on the knowledge base, a reward function with immediate rewards and delayed rewards is designed to handle sparse reward problems. Also, a list of object states is produced by retrieving the knowledge base, as a standard to define the quality of action sequences. Experimental results demonstrate that our approach yields good performance on accuracy of action sequences production.
In this study, we improved an underactuated finger mechanism by using Solidworks to simulate the grasp operation of a finger in some different situations. In addition, a robot palm is designed for the three-finger robot hand with the designed underactuated fingers. A Solidworks simulation was used to verify the rationality of the design. Some parts of the hand were modified to fit for 3D printing, and a prototype of the hand was produced by 3D printing, which could reduce the cost of the production process, as well as provide design flexibility and other advantages. Finally, some grasping experiments were made with the prototype. The results showed that the robot could grasp objects with different sizes, and further verified the rationality of the design and feasibility of fabricating the robot hand using 3D printing.
The aim of this paper is to study complex modified projective synchronization (CMPS) between fractional-order chaotic nonlinear systems with incommensurate orders. Based on the stability theory of incommensurate fractional-order systems and active control method, control laws are derived to achieve CMPS in three situations including fractional-order complex Lorenz system driving fractional-order complex Chen system, fractional-order real Rössler system driving fractional-order complex Chen system, and fractionalorder complex Lorenz system driving fractional-order real Lü system. Numerical simulations confirm the validity and feasibility of the analytical method.
In this paper, the state-feedback Nash game based mixed H2/H∞ design[
Wired drill pipe (WDP) technology is one of the most promising data acquisition technologies in today's oil and gas industry. For the first time it allows sensors to be positioned along the drill string which enables collecting and transmitting valuable data not only from the bottom hole assembly (BHA), but also along the entire length of the wellbore to the drill floor. The technology has received industry acceptance as a viable alternative to the typical logging while drilling (LWD) method. Recently more and more WDP applications can be found in the challenging drilling environments around the world, leading to many innovations to the industry. Nevertheless most of the data acquired from WDP can be noisy and in some circumstances of very poor quality. Diverse factors contribute to the poor data quality. Most common sources include mis-calibrated sensors, sensor drifting, errors during data transmission, or some abnormal conditions in the well, etc. The challenge of improving the data quality has attracted more and more focus from many researchers during the past decade.This paper has proposed a promising solution to address such challenge by making corrections of the raw WDP data and estimating unmeasurable parameters to reveal downhole behaviors. An advanced data processing method, data validation and reconciliation (DVR) has been employed, which makes use of the redundant data from multiple WDP sensors to filter/remove the noise from the measurements and ensures the coherence of all sensors and models. Moreover it has the ability to distinguish the accurate measurements from the inaccurate ones. In addition, the data with improved quality can be used for estimating some crucial parameters in the drilling process which are unmeasurable in the first place, hence provide better model calibrations for integrated well planning and realtime operations.
There is significant concern that technological advances, especially in robotics and artificial intelligence (AI), could lead to high levels of unemployment in the coming decades. Studies have estimated that around half of all current jobs are at risk of automation. To look into this issue in more depth, we surveyed experts in robotics and AI about the risk, and compared their views with those of non-experts. Whilst the experts predicted a significant number of occupations were at risk of automation in the next two decades, they were more cautious than people outside the field in predicting occupations at risk. Their predictions were consistent with their estimates for when computers might be expected to reach human level performance across a wide range of skills. These estimates were typically decades later than those of the non-experts. Technological barriers may therefore provide society with more time to prepare for an automated future than the public fear. In addition, public expectations may need to be dampened about the speed of progress to be expected in robotics and AI.