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Transmission line construction is a work with high safety risks and heavy tasks. The traditional manual method for measuring line sag is influenced by objective factors, such as terrain, weather, and span, and has certain limitations in extreme environments. In addition, spacer rods are the most hardware components in transmission line construction, and the manual measurement of the installation position of conductor spacer consumes a lot of manpower. This chapter proposes a robot that can move directly on the transmission line, which can achieve measure sag value during the process of transmission line stringing construction and also can mark the installation position of the conductor spacer during the installation process. This chapter introduces the design of the structure, control system, sag measurement method, and implementation of the conductor spacer marking function. Finally, the test results of the robot on site and subsequent work are introduced.

The paper proposes a dynamics model basing on the BP algorithm. The control scheme is an efficient combination of a computed-torque control and a neural network as a compensation structure, which enhances the adaptability of controller. Though there are errors during the modeling for the robot dynamics, they can be compensated according to the rule of adjusting weight values. The controller has obtained the ideal result both in theory and in experiment.

A reactor is a pressurized core safety instrument in a NPP(Nuclear Power Plant). It is made of carbon steel and its inner surface is covered with stainless steel. Its body consists of three cylinders and they are welded to each other. There are several holes for inlets and outlets in the reactor body. The welding areas are vulnerable points for a reactor. The inspection of a rector welding areas is one of the most important maintenance procedures during a NPP overhaul period. We developed a underwater reactor welding area UT(Ultra-sonic Test) inspection system for a pre-service. The main components of the inspection system are an underwater inspection robot, a laser positioner, a UT analysis system and a main control system. We executed an underwater UT inspection for the Ulchin unit 6 at Doosan Heavy Industries with the developed inspection system.

A strategy for working with incomplete information is called competitive if it solves each problem instance at a cost not exceeding the cost of an optimal solution (with full information available), times a constant. This paper strives to demonstrate why competitive strategies are useful for the design of autonomous robots. They guarantee a good worst-case behaviour, they are easy to implement, and they allow to deal with some problems whose optimal solution would be NP-hard. We survey competitive strategies for the following problems. How to find a door in a long wall, how to find a goal in an unknown environment, how to find a point from which an unknown environment is fully visible, and how to determine a robot's location on a known map from local visibility.

A stereovision algorithm is proposed for visual odometry to estimate motion of mobile robot by providing feature pair sequence. It is composed of feature extracting, matching and tracking. Firstly, corners are extracted as features by Harris operator and grid-based optimizing. In feature matching and tracking, serious problems are caused by variable illumination between stereo images. An improved Moravec's Normalized Cross Correlation (MNCC) algorithm is presented to reduce illumination affect in computing correspondence of corners. On current stereo image pair, extracted corners are matched by correlation-based bidirectional algorithm and outliers are rejected by epipolar constraint. Matched corners are tracked in pre-estimated search windows. The computational cost is greatly reduced by limiting number of corners, pre-estimating search window and feature local-updating. Simulation results validate that our algorithm is efficient and reliable.

Currently, mechanization and stiff man-machine interaction of robots severely affects the acceptance of users, which communicate with people through simple language, images and other models, lacking understanding and expression of human emotion cognition. This paper designs the accompanying robot system with human-computer interaction based on emotion recognition. The robot can recognize changes of people's emotions and intentions and adjust its status by using multi-source information fusion of the basic physiological information and the facial expression. Therefore, it can better understand the user's intention, and their psychological changes, to meet the functional requirements accompanying emotion of users.

The miniature reconnaissance robot has great advantages in reconnaissance tasks albeit with low mobility in disaster or emergency response. In order to solve this problem, this paper develops a miniature reconnaissance robot with an air dispersing mode. The antiimpact mechanism on the robot was careful designed to prevent damage after air dispersal. The control system of the robot has also been optimized to ensure that the robot can avoid obstacles on its own. The experimental results demonstrate the robot’s effectiveness with regards to withstanding landing impact and obstacle avoidance; therefore the robot can be successfully utilized to conduct reconnaissance missions after dispersing from airplanes using a release device and parachute.

In order to establish data interaction between two physically isolated networks, this paper develops a horizontal joint robot system to automatically load and unload the CD disk for CD recording operations. A FPGA-based controller is constructed as the core control module of the system and the B-spline curve is used for the trajectory planning of the robot to improve the efficiency of the system. The experimental results demonstrate that the developed robot system adequately meets the demand of safe data interaction and the trajectory planning method improves the system’s control precision and efficiency.

Miniature reconnaissance ground robots have the potential to be widely used for information collection in disaster zones. This paper presents the control and mechanical design of a novel six-wheeled robot BMS3-6 from Beijing Institute of Technology. The robot is developed to have anti-impact capabilities to adapt to remote launching from the air through the integration of a designed eject module. The impact resistance test and the remote launch experiment validate the effectiveness of the design.

A Magnetic Switchable Device (MSD) is a ferromagnetic circuit using permanent magnets where the flux can circulate between different paths when its configuration is changed. This routes or cancels the flux trough specific surfaces, and thus turns on or off adhesion forces. We present classic and innovative magnetic configuration to realize powerful MSD. We designed and prototyped some miniature systems and give their characteristics. Finally various robotics applications for gripper, anchor and climbing robot are unveiled where the MSD solution has proved to be advantageous.

Cy-mag^3D is a miniature climbing robot with advanced mobility and magnetic adhesion. It is very compact: a cylindrical shape with 28 mm of diameter and 62 mm of width. Its design is very simple: two wheels, hence two degrees of freedom, and an advanced magnetic circuit. Despite its simplicity, Cy-mag^3D has an amazing mobility on ferromagnetic sheets. From an horizontal sheet, it can make transition to almost any intersecting sheet from 10° to 360° – we baptise the last one surface flip. It passes inner and outer straight corners in any almost inclination of the gravity. Cy-mag^3D opens new possibilities to use mobile robots for industrial inspection with stringent size limitations, as found in generators. A patent is pending on this system.

Magnetic wheels are a powerful solution to design inspection climbing robots with excellent mobility. Magnetic wheels optimization based on simulations and the results that were obtained on prototypes are presented. The measured adhesion was doubled between the classic configuration and a novel multilayer one sharing exactly the same four magnets and the same total volume of iron. This know-how is then applied to optimize magnetic wheels for the existing robot called MagneBike. The adhesion force has been multiplied by 2 to 3 times depending on the conditions. Those amazing improvements open new possibilities for miniaturization of climbing robots or payload's increase.

This paper introduces ATRIAS 2.0, a new platform for the study of bipedal locomotion in robots. One of the purposes of the robot is to further explore the role of compliance in achieving energetically efficient and agile locomotion. A second purpose is to inspire the development and experimental validation of analytical feedback control algorithms for dynamic 3D locomotion. A third purpose is to address the challenge of tightly integrating hardware and software to achieve extreme robustness to unknown ground-height variations while walking or running.

ATRIAS 1.0 is a spring-legged, monopod robot designed and built as a prototype towards a human-scale 3D biped. The monopod has to meet certain requirements concerning locomotion dynamics and energy efficiency to meet the goal of a biped that can autonomously walk and run efficiently and robustly outdoors, untethered over realistic (non-ideal) terrain. The design of ATRIAS 1.0 includes adequate control authority for robust locomotion as well as incorporating the idea of passive dynamics for high energy economy. Towards this effort, the passive dynamics of ATRIAS 1.0 are designed to match the key features of the Spring Loaded Inverted Pendulum model: a massless leg, mass centered at the hip joint, and a series spring between the ground and the mass at the hip joint. In this paper the authors discuss the key features of this unique robot design.

Controlling the locomotion of kinematically complex robots is a challenging task because different control approaches are needed to operate safely and efficiently in changing environments. This paper presents a graph-based behavior description which allows to dynamically replace behaviors on a robotic system. In the proposed approach, every behavior is represented as a directed graph that can be encoded into a data block which can be saved to or loaded from a behavior library. Since this is not a precompiled module like in other systems, the algorithm and parameters of a behavior can still be adapted online by modifying the data that represents the behavior. Thus, machine learning algorithms can optimize an existing behavior to an unknown situation, e.g., a new environment or a motor failure. With a first implementation, it is shown that the proposed behavior graphs are suited for controlling kinematically complex walking machines.

Within this paper, several robot organisms assembled from multiple CoSMO modules are presented. CoSMO is a mobile modular self-reconfigurable robot platform, capable of working autonomously on its own or connected to other modules building a robot organism. The paper provides an introduction of the possibilities and the diversity of the CoSMO platform and the resulting capabilities to solve different tasks depending on the circumstances.

The paper proposes a reconfigurable space truss assembly robot of 6 degrees of freedom. It can change its degrees of freedom flexibly through assembling with other reconfigurable robots to accomplish the multi-target optimization on the basis of finishing the assembling of the large space truss structure. The PIK algorithm is used to realize different objections' optimization, such as avoiding the singular configurations of robots, minimizing the joint torque and avoiding obstacles and even suppressing vibration of the object operated by robots. The simulation has been done in Matlab/Simulink about the assembly part's translation following the planned route and it also realizes the vibration suppression of the large space truss structure. The result shows that the amplitude of the vibration can reduce to 5.56% and the assembly has sufficient accuracy. The reconfigurable robot and method can apply to the LSTS assembling task on-orbit to realize multi-objective optimization.

Reinforcement Learning (RL) is commonly used in learning new skills or adapting new situations for humanoid robots as task or environment changes. However, new skills acquisition through RL usually starts from a state of tabula rasa, which is a time-exhausted procedure and can not be suitable for real physical robot, especially when task is a complicated one. Thus, with the emphasis on skills learning efficiency, utilizing past knowledge or experience instead of from a tabula rasa becomes a hot topic. Knowledge transfer learning with RL has exhibited low sample complexity and then been heavily focused. In this research, underling the knowledge transfer learning on autonomous robot, the problem of autonomously modeling inter-task mapping between source task and target task is addressed, where a three-way Restricted Boltzmann Machine (RBM) is employed. To further decrease the sample complexity of transfer leaning on humanoid robots, biased sampling technique is proposed instead of random sampling. To evaluate the performance of the contribution of this research, experiments are performed on a physical robot, PKU-HR5.1, with task domain of bipedal walking on both flat and slope surfaces. Experimental results demonstrate the effectiveness and efficiency of the proposed approach.

Based on the research on optical fiber gyro realize robot precision localization system, the composition of the robot positioning system were introduced first, and then introduced the basic principle of optical fiber gyro and performance indicators. Finally, the parameter was tested of fiber optic gyroscope. Results show that the fiber optic gyro parameters satisfy the requirement of the robot positioning.