A new coverage path planning (CPP) algorithm, namely cell permeability-based coverage (CPC) algorithm, is proposed in this paper. Unlike the most CPP algorithms using approximate cellular decomposition, the proposed algorithm achieves exact coverage with lower coverage overlap compared to that with the existing algorithms. Apart from a formal analysis of the algorithm, the performance of the proposed algorithm is compared with two representative approximate cellular decomposition-based coverage algorithms reported in the literature. Results of demonstrative experiments on a TurtleBot mobile robot within the robot operating system/Gazebo environment and on a Fire Bird V robot are also provided.

In this paper, a new approach is presented for perfect torque compensation of the robot in point-to-point motions. The proposed method is formulated as an open-loop optimal control problem. The problem is defined as optimal trajectory planning with adjustable design parameters to compensate applied torques of a planar 5R parallel robot for a given task, perfectly. To illustrate the effectiveness of the approach, the obtained optimal path is used as the reference command in the experiment. The experimental outputs show that the performance index has been reduced by over 80% compared to the typical design of the robot.

This paper presents a robust adaptive impedance controller for electrically driven robots using polynomials of degree N as a universal approximator. According to the universal approximation theorem, polynomials of degree N can approximate uncertainties including un-modeled dynamics and external disturbances. This fact is completely discussed and proved in this paper. The polynomial coefficients are estimated based on the adaptive law calculated in the stability analysis. A performance evaluation has been carried out to verify satisfactory performance of the controller. Simulation results on a two degree of freedom manipulator have been presented to guarantee its successful implementation.

A versatile architecture is presented to implement autonomous vehicles. The focus idea consists of a set of standalone modules, called wireless robotic components wireless robotic components (WRCs). Each component performs a particular function by means of a radio modem interface, a processing unit, and a sensor/actuator. The components interact through a coordinator that redirects asynchronous requests to the appropriate WRCs, configuring a built-in network. The WRC architecture has been tested in marine and terrestrial platforms to perform tasks of waypoint and wall following. Results show that the tested system complies with adaptability and portability that allow conforming a variety of autonomous vehicles.

Self-localization in highly dynamic environments is still a challenging problem for humanoid robots with limited computation resource. In this paper, we propose a dual-channel unscented particle filter (DC-UPF)-based localization method to address it. A key novelty of this approach is that it employs a dual-channel switch mechanism in measurement updating procedure of particle filter, solving for sparse vision feature in motion, and it leverages data from a camera, a walking odometer, and an inertial measurement unit. Extensive experiments with an NAO robot demonstrate that DC-UPF outperforms UPF and Monte–Carlo localization with regard to accuracy.

This paper presents a novel method for modeling a 3-degree of freedom open kinematic chain using quaternions algebra and neural network to solve the inverse kinematic problem. The structure of the network was composed of 3 hidden layers with 25 neurons per layer and 1 output layer. The network was trained using the Bayesian regularization backpropagation. The inverse kinematic problem was modeled as a system of six nonlinear equations and six unknowns. Finally, both models were tested using a straight path to compare the results between the Newton–Raphson method and the network training.

High-voltage power cables are important channels for power transmission systems. Their special geographical environment and harsh natural environment can lead to many different faults. At present, such special operations in dangerous and harsh environments are performed manually, which not only has high labor intensity and low work efficiency but also has great personal safety risks. In order to solve such difficult problems, this paper studies the power maintenance robot for insulator string replacement, spacer replacement, damper and drainage plate maintenance; the basic configuration and the operation motion planning have been proposed; and the virtual prototype of the inspection maintenance robots has been developed, and then the mechanical structure of the robots has been optimized by the robot kinematics modeling and analyzed the working space based on the Monte Carlo method. The system platform, operation function, structural characteristics and related key technologies involved in the robot system development were systematically summarized; the deep integration point for the robot technology with big data, cloud computing, artificial intelligence, and ubiquitous power Internet-of-Things technologies was also discussed. Finally, the physical prototype of the insulator replacement, drainage plate tightening, and damper replacement operation robot has been developed; several experimental tests on a 220 V live line have been conducted so as to verify the robot engineering practicality; and the main development and future research direction have also been pointed out at last.

In this research, a dynamic model is first established based on screw theory and the principle of virtual work for a bilaterally symmetrical hybrid robot. By combining a novel composite error (NCE) with second-order nonsingular fast terminal sliding mode (SONFTSM) control method, a NCE-based SONFTSM dynamic control method is further presented to guarantee better trajectory tracking performance and synchronization performance simultaneously. The asymptotic convergence of proposed errors and the stability of the proposed control method have been proved theoretically. Finally, the simulation and experiment are implemented to validate the effectiveness of the proposed control method.

A new stable adaptive controller based on a neural network for underactuated systems is proposed in this paper. The control scheme has been developed for two underactuated systems as examples. The Furuta pendulum and the Inertia Wheel Pendulum (IWP) have been examined in this paper. The presented approach aims to address the control problem of the given system in swing up, stabilization, and disturbance rejection. To avoid oscillations, two adaptive neural networks (ANNs) are implemented. The first one is used to approximate the equivalent control online and the second one to minimize the oscillations.

Dynamic movement primitives (DMP) are motion building blocks suitable for real-world tasks. We suggest a methodology for learning the manifold of task and DMP parameters, which facilitates runtime adaptation to changes in task requirements while ensuring predictable and robust performance. For efficient learning, the parameter space is analyzed using principal component analysis and locally linear embedding. Two manifold learning methods: kernel estimation and deep neural networks, are investigated for a ball throwing task in simulation and in a physical environment. Low runtime estimation errors are obtained for both learning methods, with an advantage to kernel estimation when data sets are small.

This paper proposes a robust controller for the generation of stable limit cycles in multi-input mechanical systems subjected to model uncertainties. The proposed idea is based on Port-Controlled Hamiltonian (PCH) model and energy-based control by considering the Hamiltonian function as the Lyapunov function. For this purpose, first, a nominal controller is designed by shaping the energy function of the system according to the structure of the desired limit cycle. Then, an additional robustifying control term is designed based on the integral sliding mode method with the selection of an appropriate sliding surface. Finally, computer simulations for two practical case studies are provided to confirm the effectiveness of the proposed controller in the generation of stable limit cycles in the presence of uncertainties.

In this paper, mathematical models of kinematics, statics and inverse dynamics are derived firstly according to the mechanical structure of leg hydraulic drive system (LHDS). Then, all the above models are integrated with MATLAB/Simulink to build the LHDS simulation model, the model not only considers influence of leg dynamic characteristics on hydraulic system but also takes into account nonlinearity, variable load characteristics and other common problems brought by hydraulic system, and solves compatibility and operation time which brought by using multiple software simultaneously. The experimental results show the simulation model built in this paper can accurately express characteristics of the system.