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Autonomous obstacle avoidance is important in unmanned surface vehicle (USV) navigation. Although the result of obstacle detection is often inaccurate because of the inherent errors of LIDAR, conventional methods typically emphasize on a single obstacle-avoidance algorithm and neglect the limitation of sensors and safety in a local region. Conventional methods also fail in seamlessly integrating local and global obstacle avoidance algorithms. This paper aims to present a cooperative manoeuvring approach including both local and global obstacle avoidance.

The global algorithm used in our USV is the Artificial Potential Field-Ant Colony Optimization (APF-ACO) obstacle-avoidance algorithm, which plans a relative optimal path on the specified electronic map before the cruise of USV. The local algorithm is a multi-layer obstacle-avoidance framework based on a single LIDAR to present an efficient solution to USV path planning in the case of sensor errors and collision risks. When obstacles are within a layer, the USV uses a corresponding obstacle-avoidance algorithm. Then the USV moves towards the global direction according to fuzzy rules in the fuzzy layer.

The presented method offers a solution for obstacle avoidance in a complex environment. The USV follows the global trajectory planed by the APF-ACO algorithm. While, the USV can bypass current obstacle in the local region based on the multi-layer method effectively. This fact was validated by simulations and field trials.

The method presented in this paper takes advantage of algorithm integration that remedies errors of obstacle detection. Simulation and experiments were also conducted for performance evaluation.

This paper aims to improve the adaptability and control performance of the controller, a proposed seeker optimization algorithm (SOA) is introduced to optimize the controller parameters of a robot manipulator.

In this paper, a traditional proportional integral derivative (PID) controller and a fuzzy logic controller are integrated to form a fuzzy PID (FPID) controller. The SOA, as a novel algorithm, is used for optimizing the controller parameters offline. There is a performance comparison in terms of FPID optimization about the SOA, the genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO). The DC motor model and the experimental platform are used to test the performance of the optimized controller.

Compared with GA, PSO and ACO, this novel optimization algorithm can enhance the control accuracy of the system. The optimized parameters ensure a system with faster response speed and better robustness.

A simplified FPID controller structure is constructed and a novel SOA method for FPID controller is presented. In this paper, the SOA is applied on the controller of 5-DOF manipulator, and the validation of controllers is tested by experiments.

The purpose of this paper was to solve the shortage of carrying energy in probing robot and make full use of wind resources in the Antarctic expedition by designing a four-wheel land-yacht. Land-yacht is a new kind of mobile robot powered by the wind using a sail. The mathematical model and trajectory of the land-yacht are presented in this paper.

The mechanism analysis method and experimental modeling method are used to establish a dual-input and dual-output mathematical model for the motion of land-yacht. First, the land-yacht’s model structure is obtained by using mechanism analysis. Then, the models of steering gear, servomotors and force of wing sail are analyzed and validated. Finally, the motion of land-yacht is simulated according to the mathematical model.

The mathematical model is used to analyze linear motion and steering motion. Compared with the simulation results and the actual experimental tests, the feasibility and reliability of the proposed land-yacht modeling are verified. It can travel according to the given signal.

This land-yacht can be used in the Antarctic, outer planet or for harsh environment exploration.

A land-yacht is designed, and the contribution of this research is the development of a mathematical model for land-yacht robot. It provides a theoretical basis for analysis of the land-yacht’s motion.

The purpose of this paper is to present results of the studies on modification of ethylene-vinyl acetate (EVA) encapsulation foil to be used as thermal interface material (TIM). It is estimated that poor thermal management in electronic devices can cause over 50 per cent of failures. As the junction temperature rises, the failure rate for electronics increases exponentially. To ensure sufficient heat transfer from its source, TIMs are used in various circuits. On the other hand, it is important to ensure high electric resistivity of the designed TIM.

The focus of the investigation was twofold: modification of EVA with both graphene oxide (GO) and silver nanopowder (nAg); and TIM applicability through lamination of photovoltaic cells with standard and modified EVA foil. The main problem of a new type of encapsulant is proper gas evacuation during the lamination process. For this reason, reference and modified samples were compared taking into account the percentage of gas bubbles in visible volume of laminated TIM. Finally, reference and modified TIM samples were compared using differential scanning calorimetry (DSC) and laser flash analysis (LFA) measurements.

The proper parameters of the lamination process for the modified EVA foil - with both GO and organometallic nAg particles - were selected. The nAg addition results in an increase in thermal conductivity of the proposed compositions with respect to unmodified EVA foil, which was confirmed by DSC and LFA measurements.

The experiments confirmed the potential application of both EVA foil as a matrix for TIM material and nAg with GO as an active agent. Proposed composition can bring additional support to a solar cell or other electronic components through effective heat removal, which increases its performance.

The heat transfer properties play significant roles in the thermal comfort of the clothing products. The purpose of this paper is to find the relationship between heat transfer properties and fabrics' structure, yarn properties and predict the effective thermal conductivity of single layer woven fabrics by a parametric mathematical model.

First, the weave unit was divided into four types of element regions, including yarn overlap regions, yarn crossing regions, yarn floating regions and pore regions. Second, the number and area proportion of each region were calculated respectively. Some formulas were created to calculate the effective thermal conductivity of each element region based on serial model, parallel model or series–parallel mixing model. Finally, according to the number and area proportion of each region in weave unit, the formulas were established to calculate the fabric overall effective thermal conductivity in thickness direction based on the parallel models.

The influences of yarn spacing, yarn width, fabric thickness, the compressing coefficients of air layers and weave type on the effective thermal conductivity were further discussed respectively. In this model, the relationships between the effective thermal conductivity and each parameter are some polynomial fitting curves with different orders. Weave type affects the change of effective thermal conductivity mainly through the numbers of different elements and their area ratios.

In this model, the formulas were created respectively to calculate the effective thermal conductivity of each element region and whole weave unit. The serial–parallel mixing characteristics of yarn and surrounding air are considered, as well as the compression coefficients of air layers. The results of this study can be further applied to the optimal design of mixture fabrics with different warp and filling yarn densities or different yarn thermal properties.

The purpose of this paper is to reduce the strain and vibration during robotic machining.

An intelligent approach based on particle swarm optimization (PSO) and adaptive iteration algorithms is proposed to optimize the PD control parameters in accordance with robotic machining state.

The proposed intelligent approach can significantly reduce robotic machining strain and vibration.

The relationship between robotic machining parameters is studied and the dynamics model of robotic machining is established. In view of the complexity of robotic machining process, the PSO and adaptive iteration algorithms are used to optimize the PD control parameters in accordance with robotic machining state. The PSO is used to optimize the PD control parameters during stable-machining state, and the adaptive iteration algorithm is used to optimize the PD control parameters during cut-into state.