Gait identification and optimisation for amphi-underwater robot by using ant colony algorithm

Manoeuvrable robot commonly has become the focus of the latest heated issues especially in applications that involved disaster rescue, military missions and underwater or extra-terrestrial explorations. Currently, the manoeuvrable robot is controlled manually by the operator and it’s a wheeled type. It is used for rescue missions to transport people from disaster area to the safe zone. However, the robot is incapable of moving automatically, and it goes through terrain or landscape like swarm. Therefore, a suitable platform is required to transport or for other uses especially in dangerous mission. It is very difficult to estimate the movement of the robot to avoid obstacles and choose the alternative path. Hence, this research presents the point-to-point gait identification or path planning of the behavious of the robot to manuever autonomously on both on-land and underwater environment. For the optimization, the robot will travel from one specific point to another with the predefined position within optimized gait and fastest time by using Ant Colony Optimization (ACO) technique. The algorithm being compared, between Ant Colony Algorithm (ACO) and the Particle Swarm Optimisation (PSO) in terms of time and distance. The ACO been chosen because of the positive feedback for rapid discovery and able to use in dynamic applications for example adapts to changes like new distances. The performance of the algorithm showed that the execution time of ACO is more realistic. Hence, Matlab is used to determine the best cost extracted from the ACO with the pre-define of number of iteration and the number of ants. The laboratory-scaled prototype for amphibious vehicle was developed to test the design controlled with ACO technique where Global Positioning System (GPS) is used for the coordination of the robot and Magnetometer for the position of the robot. The robot prototype is able to move autonomously and optimized by the ant colony optimization with predefined position and terrain condition © BEIESP

Development of particle swarm optimization based rainfall-runoff prediction model for Pahang River, Pekan

Flooding is a natural disaster which has been occurring annually throughout the whole world. The disaster, such as other natural catastrophe could only be mitigated rather than it being completely solved. Runoff prediction proved to be very vital in pre-flooding management system. In recent years, Artificial Neural Network has been applied in various prediction models of hydrological system. It is proposed to model the rainfall-runoff system of Pahang River in Pekan. Mean rainfall data of 5 hydrological stations are used as the input and water level data as the output. The Artificial Neural Networks are trained with Particle Swarm Optimization. The performances of Artificial Neural Networks were measured with Ackley cost function value. Neural network configuration of 450 number of maximum iteration, 6 number of particles and 1.9 and 2.0 values of Particle Swarm Optimization parameter constant for global best (c1) and Particle Swarm Optimization constant for personal best (c2) respectively shows the highest global best function value. The neural network configuration of 300 number of maximum iteration, 3 numbers of particles and 2.2 value of (c1) and (c2) produces lowest global best function value. The output shows Artificial Neural Network trained by Particle Swarm Optimization can successfully model rainfall-runoff. © 2016 IEEE

Electric vehicle battery modelling and performance comparison in relation to range anxiety

In electric vehicle, rechargeable battery served as energy source for all its system operation which include electric motor for propulsion system and also other auxiliary components. Therefore, it becomes an important issue to be tackled in EV technology in order to enhance the battery energy capacity for long range operation. In general public view, people tend to be very concern in purchasing the electric car. One of the concerns lies on the question of how far they can travel with only battery for their car propulsion means. Therefore, this study tries to investigate the relation between battery types and the range anxiety faces by electric car makers. The investigations reveals that, Li-ion as the battery with high energy density cover more area or distance travel

Rainfall-rinoff model based on ANN with LM, BR and PSO as learning algorithms

Rainfall-runoff model requires comprehensive computation as its relation is a complex natural phenomenon. Various inter-related processes are involved with factors such as rainfall intensity, geomorphology, climatic and landscape are all affecting runoff response. In general there is no single rainfallrunoff model that can cater to all flood prediction system with varying topological area. Hence, there is a vital need to have custom-tailored prediction model with specific range of data, type of perimeter and antecedent hour of prediction to meet the necessity of the locality. In an attempt to model a reliable rainfall-runoff system for a flood-prone area in Malaysia, 3 different approach of Artificial Neural Networks (ANN) are modelled based on the data acquired from Sungai Pahang, Pekan. In this paper, the ANN rainfall-runoff models are trained by the Levenberg Marquardt (LM), Bayesian Regularization (BR) and Particle Swarm Optimization (PSO). The performances of the learning algorithms are compared and evaluated based on a 12-hour prediction model. The results demonstrate that LM produces the best model. It outperforms BR and PSO in terms of convergence rate, lowest mean square error (MSE) and optimum coefficeint of correlation. Furthermore, the LM approach are free from overfitting, which is a crucial concern in conventional ANN learning algorithm. Our case study takes the data of rainfall and runoff from the year 2012 to 2014. This is a case study in Pahang river basin, Pekan, Malaysia

Rainfall-runoff model based on ANN with LM, BR and PSO as learning algorithms

Rainfall-runoff model requires comprehensive computation as its relation is a complex natural phenomenon. Various inter-related processes are involved with factors such as rainfall intensity, geomorphology, climatic and landscape are all affecting runoff response. In general there is no single rainfall-runoff model that can cater to all flood prediction system with varying topological area. Hence, there is a vital need to have custom-tailored prediction model with specific range of data, type of perimeter and antecedent hour of prediction to meet the necessity of the locality. In an attempt to model a reliable rainfall-runoff system for a flood-prone area in Malaysia, 3 different approach of Artificial Neural Networks (ANN) are modelled based on the data acquired from Sungai Pahang, Pekan. In this paper, the ANN rainfall-runoff models are trained by the Levenberg Marquardt (LM), Bayesian Regularization (BR) and Particle Swarm Optimization (PSO). The performances of the learning algorithms are compared and evaluated based on a 12-hour prediction model. The results demonstrate that LM produces the best model. It outperforms BR and PSO in terms of convergence rate, lowest mean square error (MSE) and optimum coefficient of correlation. Furthermore, the LM approach are free from overfitting, which is a crucial concern in conventional ANN learning algorithm. Our case study takes the data of rainfall and runoff from the year 2012 to 2014. This is a case study in Pahang river basin, Pekan, Malaysia

Locomation strategies for amphibious robots-a review

In the past two decades, unmanned amphibious robots have proven the most promising and efficient systems ranging from scientific, military, and commercial applications. The applications like monitoring, surveillance, reconnaissance, and military combat operations require platforms to maneuver on challenging, complex, rugged terrains and diverse environments. The recent technological advancements and development in aquatic robotics and mobile robotics have facilitated a more agile, robust, and efficient amphibious robots maneuvering in multiple environments and various terrain profiles. Amphibious robot locomotion inspired by nature, such as amphibians, offers augmented flexibility, improved adaptability, and higher mobility over terrestrial, aquatic, and aerial mediums. In this review, amphibious robots' locomotion mechanism designed and developed previously are consolidated, systematically The review also analyzes the literature on amphibious robot highlighting the limitations, open research areas, recent key development in this research field. Further development and contributions to amphibious robot locomotion, actuation, and control can be utilized to perform specific missions in sophisticated environments, where tasks are unsafe or hardly feasible for the divers or traditional aquatic and terrestrial robots

Analysis and practical validation on a multi-linkage scissor platforms drive system for the satellite test facilities

This paper evaluates a modified structural analysis in measuring the reaction forces on the multi-linkage scissor mechanism driven by a ball-screw system. The proposed structural-virtual work (SV) analysis takes into account all reaction forces on the designed linkages to evaluate the accurate sizing of the actuator and as the consequence, the overall machinery development cost will be significantly reduced. The idea is proven in three ways: analytical analysis, simulation analysis, and experimental analysis based on the developed prototype. The simulation study has shown that the estimated torque is successfully reduced by 29% as compared to the conventional approach. The superiority of the proposed analysis is confirmed by 12% error between the simulation and results from the developed prototype. The successful method proposed in this paper can be further used for all multi-linkage systems in the heavy-vehicle industry that require accurate sizing of the actuators

Design and modelling of four-legged amphibious robot

This paper proposed a method to model the gait movement of four-legged amphibious robot. This type of robot has shown a great potential to perform complex operations in difficult and challenging land and underwater environments. Not only they can monitor and manipulate complicated environment conditions during disasters such as floods, landslides, and others, but it can also perform deep ocean exploration, underwater structures manipulation, disaster rescue operations, and reconnaissance. The promising advantages of amphibious underwater robots have motivated researchers to propose different design strategies for the structures and control methods of such vehicles. To design and model the fourlegged amphibious robot, the connection between the input links with the output links was identified in this paper. The system architecture and system prototype were developed for model performance test. The tests were conducted and analyses using the SAM- the Ultimate Mechanism Designer for various configurations of the links in terms of the angle, angular velocity and the angular acceleration

Robustness analysis of fractional order PID for an electrical aerial platform

This work was performed to objectively measure and assess the robustness and tracking performance of fractional order of proportional, integral and derivative (FOPID) controller as compared to the conventional PID control. In satellite research and development, the satellite undergoes numerous tests such as thermal, acoustic and vibration tests in the cleanroom environment. However, due to space limitation in the cleanroom and the sensitive components of the satellite, it requires vibration-free, smooth and precise motion when handling the satellite. In addition, measurement interference might occur due to cable routing during procedures or tasks performed by an operator. Unlike the previous work, the robustness analysis of FOPID controller was not systematically conducted. In this paper, the analysis took into account the actuator dynamics, and various tests were considered to measure the robustness of FOPID controller. The designed FOPID controller was implemented on the scissor-type lifting mechanism of motorized adjustable vertical platform (MAVeP) model, and its performance was compared with the traditional PID controller. A comprehensive verification using MATLAB and Solidworks was carried out to generate the model and conduct the analysis. Both controllers were initially tuned using Nichol-Ziegler technique, and the additional FOPID controller parameters was tuned using the Astrom-Hagglund method. From the simulation work, it was found that the FOPID controller’s tracking error was reduced between 10 % - 50 % for the disturbance rejection tests and reference to disturbance ratio (RDR) spectrum was higher as compared to PID. The analysis in this paper was predicted to be the main driver to implement FOPID controller in the complex system in the industry, especially for sensitive material handling and transportation such as satellite

Optimizing tensile strength of PLA-Lignin Bio-composites using machine learning approaches

It is imperative to accurately estimate the final performance of composite parts during the initial design phase of the manufacturing process. In generating sustainable bio composites with superior mechanical properties such as tensile strength, the combination of fillers and plasticizers, as well as their concentration in the mixture, are always deemed crucial. In order to reduce the number of experimental runs and their associated costs and timescales, statistical optimization of the core design elements has become increasingly important. The filler and plasticizer concentrations of extruded bio composites were adjusted in this study utilizing both statistical (analysis of variance (ANOVA) and response surface methodology (RSM)) and machine learning (Multilayer Perceptron (MLP)) approaches. Initial ANOVA results indicated that lignin, epoxidized palm oil (EPO), and their respective combinations were the most influential factors in enhancing the durability of lignin/polylactic acid (PLA) bio composites. In this work, RSM and MLP were used to model and predict the data in order to maximize the various solutions and establish the nonlinear relationship between the concentration of lignin and EPO