Mobile Robot Path Planning in an Obstacle-free Static Environment using Multiple Optimization Algorithms

This article presents the implementation and comparison of fruit fly optimization (FOA), ant colony optimization (ACO) and particle swarm optimization (PSO) algorithms in solving the mobile robot path planning problem. FOA is one of the newest nature-inspired algorithms while PSO and ACO has been in existence for a long time. PSO has been shown by other studies to have long search time while ACO have fast convergence speed. Therefore there is need to benchmark FOA performance with these older nature-inspired algorithms. The objective is to find an optimal path in an obstacle free static environment from a start point to the goal point using the aforementioned techniques. The performance of these algorithms was measured using three criteria: average path length, average computational time and average convergence speed. The results show that the fruit fly algorithm produced shorter path length (19.5128 m) with faster convergence speed (3149.217 m/secs) than the older swarm intelligence algorithms. The computational time of the algorithms were in close range, with ant colony optimization having the minimum (0.000576 secs). Keywords:  Swarm intelligence, Fruit Fly algorithm, Ant Colony Optimization, Particle Swarm Optimization, optimal path, mobile robot

Multi-camera Realtime 3D Tracking of Multiple Flying Animals

Automated tracking of animal movement allows analyses that would not otherwise be possible by providing great quantities of data. The additional capability of tracking in realtime - with minimal latency - opens up the experimental possibility of manipulating sensory feedback, thus allowing detailed explorations of the neural basis for control of behavior. Here we describe a new system capable of tracking the position and body orientation of animals such as flies and birds. The system operates with less than 40 msec latency and can track multiple animals simultaneously. To achieve these results, a multi target tracking algorithm was developed based on the Extended Kalman Filter and the Nearest Neighbor Standard Filter data association algorithm. In one implementation, an eleven camera system is capable of tracking three flies simultaneously at 60 frames per second using a gigabit network of nine standard Intel Pentium 4 and Core 2 Duo computers. This manuscript presents the rationale and details of the algorithms employed and shows three implementations of the system. An experiment was performed using the tracking system to measure the effect of visual contrast on the flight speed of Drosophila melanogaster. At low contrasts, speed is more variable and faster on average than at high contrasts. Thus, the system is already a useful tool to study the neurobiology and behavior of freely flying animals. If combined with other techniques, such as `virtual reality'-type computer graphics or genetic manipulation, the tracking system would offer a powerful new way to investigate the biology of flying animals.Comment: pdfTeX using libpoppler 3.141592-1.40.3-2.2 (Web2C 7.5.6), 18 pages with 9 figure

An optimized fractional order PID controller for suppressing vibration of AC motor

Fractional order Proportional-Integral-Derivative (PID) controller is composed of a number of integer order PID controllers. It is more accurate to control the complex system than the traditional integer order PID controller. The values of parameters of the fractional order PID controller play a decisive role for the control effect. Because the fractional order PID controller added two adjustable parameters than the traditional PID controller, it is very difficult to tune parameters. So the Back Propagation (BP) neural network is selected to optimize the parameters of the fractional order PID controller in order to obtain the high performance. Then the optimized fractional order PID controller and the traditional PID controller are used to control AC motor speed governing system. And the vibration spectrum and stator current spectrum under different rotating speeds are compared and analyzed in detail. The results show that the optimized fractional order PID controller has better vibration suppression performance than the traditional PID controller. The reason is that the optimized fractional order PID controller changed the stator current component, and further changed the frequency components and the amplitude of the vibration signal of the motor

Prediction of Spot Price of Iron Ore Based on PSR-WA-LSSVM Combined Model

Aiming at the problems that the existing single time series models are not accurate and robust enough when it comes to forecasting the iron ore prices and the parameters of the traditional LSSVM model are difficult to determine, we propose a combined model based on Phase Space Reconstruction (PSR), wavelet transform and LSSVM (PSR-WA-LSSVM) to tackle these issues. ARIMA model, LSTM model, PSR-LSSVM model, and PSR-WA-LSSVM models were used for contrast simulation to forecast the spot price data of 61.5%PB powder from January 30, 2019, to February 1, 2021, in Ningbo Zhoushan port. The experimental results show that the PSR-WA-LSSVM combination model achieves better prediction results. At the same time, the model has a good performance in the multistep prediction of the iron ore price

A Nature inspired guidance system for unmanned autonomous vehicles employed in a search role.

Since the very earliest days of the human race, people have been studying animal behaviours. In those early times, being able to predict animal behaviour gave hunters the advantages required for success. Then, as societies began to develop this gave way, to an extent, to agriculture and early studies, much of it trial and error, enabled farmers to successfully breed and raise livestock to feed an ever growing population. Following the advent of scientific endeavour, more rigorous academic research has taken human understanding of the natural world to much greater depth. In recent years, some of this understanding has been applied to the field of computing, creating the more specialised field of natural computing. In this arena, a considerable amount of research has been undertaken to exploit the analogy between, say, searching a given problem space for an optimal solution and the natural process of foraging for food. Such analogies have led to useful solutions in areas such as numerical optimisation and communication network management, prominent examples being ant colony systems and particle swarm optimisation; however, these solutions often rely on well-defined fitness landscapes that may not always be available. One practical application of natural computing may be to create behaviours for the control of autonomous vehicles that would utilise the findings of ethological research, identifying the natural world behaviours that have evolved over millennia to surmount many of the problems that autonomous vehicles find difficult; for example, long range underwater navigation or obstacle avoidance in fast moving environments. This thesis provides an exploratory investigation into the use of natural search strategies for improving the performance of autonomous vehicles operating in a search role. It begins with a survey of related work, including recent developments in autonomous vehicles and a ground breaking study of behaviours observed within the natural world that highlights general cooperative group behaviours, search strategies and communication methods that might be useful within a wider computing context beyond optimisation, where the information may be sparse but new paradigms could be developed that capitalise on research into biological systems that have developed over millennia within the natural world. Following this, using a 2-dimensional model, novel research is reported that explores whether autonomous vehicle search can be enhanced by applying natural search behaviours for a variety of search targets. Having identified useful search behaviours for detecting targets, it then considers scenarios where detection is lost and whether natural strategies for re-detection can improve overall systemic performance in search applications. Analysis of empirical results indicate that search strategies exploiting behaviours found in nature can improve performance over random search and commonly applied systematic searches, such as grids and spirals, across a variety of relative target speeds, from static targets to twice the speed of the searching vehicles, and against various target movement types such as deterministic movement, random walks and other nature inspired movement. It was found that strategies were most successful under similar target-vehicle relationships as were identified in nature. Experiments with target occlusion also reveal that natural reacquisition strategies could improve the probability oftarget redetection

Study on a novel fault diagnosis method based on integrating EMD, fuzzy entropy, improved PSO and SVM

In order to effectively improve the fault diagnosis accuracy of motor bearing, a new fault diagnosis method based on integrating empirical mode decomposition(EMD), fuzzy entropy, improved particle swarm optimization(PSO) algorithm and support vector machine (SVM) is proposed in this paper. In the proposed fault diagnosis method, the EMD method is used to decompose vibration signals into a series of basic intrinsic mode functions (IMFs). Then the fuzzy entropy is used to effectively extract the features of vibration signal, which are regarded as input vectors of SVM. The dynamic adjustment strategy of arctangent function of learning factor, decreasing inertia weight of function and adaptive mutation strategy of particles are used to improve the basic PSO algorithm in order to avoid premature convergence, escape from falling into the local optimal value and improve the optimization performance. And the improved PSO algorithms are selected to optimize the parameters of SVM in order to improve the generalization ability and the classification accuracy. And then a new fault diagnosis method is obtained. Finally, the actual vibration signals of motor bearing are selected to verify the effectiveness of the proposed fault diagnosis method. The experiment results show that the improved PSO algorithm can effectively obtain the optimal combination values of parameters of SVM, and the proposed fault diagnosis method can accurately and quickly diagnose the faults of motor bearing with the higher reliability. And it provides a new idea based on making full use of the advantages of each method for studying motor fault diagnosis

Determination of Time Dependent Stress Distribution on Potato Tubers at Mechanical Collision

This study focuses on determining internal stress progression and the realistic representation of time dependent deformation behaviour of potato tubers under a sample mechanical collision case. A reverse engineering approach, physical material tests and finite element method (FEM)-based explicit dynamics simulations were utilised to investigate the collision based deformation characteristics of the potato tubers. Useful numerical data and deformation visuals were obtained from the simulation results. The numerical results are presented in a format that can be used for the determination of bruise susceptibility magnitude on solid-like agricultural products. The modulus of elasticity was calculated from experimental data as 3.12 [MPa] and simulation results showed that the maximum equivalent stress was 1.40 [MPa] and 3.13 [MPa] on the impacting and impacted tubers respectively. These stress values indicate that bruising is likely on the tubers. This study contributes to further research on the usage of numerical-methods-based nonlinear explicit dynamics simulation techniques in complicated deformation and bruising investigations and industrial applications related to solid-like agricultural products