Vol 52, No 5 (2018)

This paper investigates the interpolation model predictive control (MPC) algorithm for nonlinear discrete-time systems, which can be represented by affine linear parameter varying (LPV) model. The general nonlinear model is transformed into the quasi-LPV model, then the equivalent polytopic LPV model and disturbed Linear time-invariant (LTI) model are obtained. Therefore, a finite-horizon interpolation MPC algorithm based ellipsoidal invariant set (EIS) is proposed. For comparison, the existing zero-horizon interpolation MPC algorithm, based on EIS, is also described to display the advantages of proposed algorithm. By virtue of the finite-horizon technique, the feasible region of proposed algorithm is much larger than zero-horizon interpolation MPC algorithm. An illustrative example is given to verify the effectiveness of proposed algorithms.

The paper discuses several options to implement multi-robot systems focusing on coordination and action planning of the system. Particular implementation described in details as a case study presents a solution used in multi-robot system for feed pushing in cattle farm. The paper describes implementation of behavior based robot team control and two different path planning methods ensuring that the feed pushing work is done properly. Conclusions outline suggestions for practical implementations regardless of particular application domain.

The paper proposes flexible system that is based on Robot Operating System framework for integration of 3D computer vision and artificial intelligence algorithms with industrial robots for automation of industrial tasks. The system provides flexibility of 3D computer vision hardware and industrial robot components, allowing to test different hardware with small software changes. The experimental system consisting of Kinect V2 RGB+Depth camera and Universal Robots UR5 robot was set up. In experimental setup the pick and place task was implemented where randomly organized two types of objects (tubes and cans) where picked from the container and sorted in two separate containers. Average full cycle time for the task was measured to be 19.675 s.

Recently, link prediction techniques have been increasingly adopted to discover link patterns in various domains. On challenging problem is to improve the performance continually. In this paper, we propose a recursive prediction mechanism to addresses the link prediction problem. A posterior is calculated based on observed data, and then we estimate the state of the graph and use the posterior as the prior distribution for the next stage. With the increasing of iterations, the proposed approach incorporates more and more topological structure information and node attributes data. Experimental results with real-world networks have shown that the proposed solution performs better in terms of well-known metrics as compared to the existing approaches. This novel approach has already been integrated into an expert system and provides auxiliary support for decision-makers.

With the rapid development of economic and scientific levels, urban traffic in China has been further improved. Moreover, the number of private cars is increasing because of the improvement of living standard. However, various traffic problems such as traffic jam, traffic accidents, disordered traffic order and unreasonable travel structure come along. Therefore, the utilization and development of urban intelligent traffic is an inevitable choice for the improvement of Chinese traffic. This study mainly investigated the application of wireless sensor network in urban intelligent traffic information acquisition. Differential Time of Arrival (DTOA) technology, least square method and Kalman filter algorithm were used to improve the location accuracy of vehicles. Moreover, Matlab simulation experiment was performed. The method proposed in this study can improve the acquisition speed of information and positioning accuracy of vehicles. This work is beneficial to the solution of traffic disorder.

In Printed Circuit Board (PCB) drilling machines, the location of the drill holes are fed into the machine and the PCB will be drilled at the corresponding coordinates. Some machines do not choose the optimal route when completing their tasks. Hence, this paper proposes an approach, which is based on the Algorithm Shortest Path Search Algorithm (SPSA), for finding the optimal route in PCB holes drilling process. In SPSA, when the robotic arm at the initial position, the algorithm calculates the nearest point to the initial position from all points that the wires start or ends with. If the nearest point is a start-of-wire point, it will use SPS algorithm 1. If the nearest point is an end-of-wire point, it will use SPS algorithm 2. This process is repeated until drilling all the lines. Then, the robotic arm will drill all the holes according to the proposed Simulated Annealing Algorithm (AS) in order to determine the optimal machining parameters for milling operations. The results of the different optimization algorithms Genetic Algorithm (GA) and AS are compared and conclusions are presented. The proposed Computer Numerical Control (CNC) machine consists of a driver, drill, three stepper motors, cables and microcontroller PIC16f877A to control the movement of the machine. The SPSA algorithm optimizes the use of the motors and other mechanical paths involved in the process while reducing total time taken to traverse all the drill holes. This paper also explains the detailed problem of interest and the mathematical formulation of the problem is defined. Experimental result indicates that the proposed SPSA-based approach is capable to efficiently find the optimal route for PCB holes drilling process.