Autonomous Navigation for Mars Exploration

The autonomous navigation technology uses the multiple sensors to percept and estimate the spatial locations of the aerospace prober or the Mars rover and to guide their motions in the orbit or the Mars surface. In this chapter, the autonomous navigation methods for the Mars exploration are reviewed. First, the current development status of the autonomous navigation technology is summarized. The popular autonomous navigation methods, such as the inertial navigation, the celestial navigation, the visual navigation, and the integrated navigation, are introduced. Second, the application of the autonomous navigation technology for the Mars exploration is presented. The corresponding issues in the Entry Descent and Landing (EDL) phase and the Mars surface roving phase are mainly discussed. Third, some challenges and development trends of the autonomous navigation technology are also addressed

IoT in smart communities, technologies and applications.

Internet of Things is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The Internet of Things (IoT) for Smart Cities has many different domains and draws upon various underlying systems for its operation, in this work, we provide a holistic coverage of the Internet of Things in Smart Cities by discussing the fundamental components that make up the IoT Smart City landscape, the technologies that enable these domains to exist, the most prevalent practices and techniques which are used in these domains as well as the challenges that deployment of IoT systems for smart cities encounter and which need to be addressed for ubiquitous use of smart city applications. It also presents a coverage of optimization methods and applications from a smart city perspective enabled by the Internet of Things. Towards this end, a mapping is provided for the most encountered applications of computational optimization within IoT smart cities for five popular optimization methods, ant colony optimization, genetic algorithm, particle swarm optimization, artificial bee colony optimization and differential evolution. For each application identified, the algorithms used, objectives considered, the nature of the formulation and constraints taken in to account have been specified and discussed. Lastly, the data setup used by each covered work is also mentioned and directions for future work have been identified. Within the smart health domain of IoT smart cities, human activity recognition has been a key study topic in the development of cyber physical systems and assisted living applications. In particular, inertial sensor based systems have become increasingly popular because they do not restrict users’ movement and are also relatively simple to implement compared to other approaches. Fall detection is one of the most important tasks in human activity recognition. With an increasingly aging world population and an inclination by the elderly to live alone, the need to incorporate dependable fall detection schemes in smart devices such as phones, watches has gained momentum. Therefore, differentiating between falls and activities of daily living (ADLs) has been the focus of researchers in recent years with very good results. However, one aspect within fall detection that has not been investigated much is direction and severity aware fall detection. Since a fall detection system aims to detect falls in people and notify medical personnel, it could be of added value to health professionals tending to a patient suffering from a fall to know the nature of the accident. In this regard, as a case study for smart health, four different experiments have been conducted for the task of fall detection with direction and severity consideration on two publicly available datasets. These four experiments not only tackle the problem on an increasingly complicated level (the first one considers a fall only scenario and the other two a combined activity of daily living and fall scenario) but also present methodologies which outperform the state of the art techniques as discussed. Lastly, future recommendations have also been provided for researchers

PID-Controller Tuning of Brushless DC Motor by Using ACO (Ant Colony Optimization) Technique

ACO stands for Ant Colony Optimization which it automatically finds the best solution or the best PID controller parameters for the targeted control system. ACO algorithm is used as the technique for the PID controller parameters optimization. It functions in finding and obtaining optimum values of gain, integral time and derivative time for the PID controller. At the testing stage, the ACO algorithm is developed by using MATLAB. The ACO algorithm will calculate the ISE (Integral Square Error) for each set of PID parameters. and then comparison is done on the different values of ISE from different sets of PID parameters to choose the best PID parameters set with the lowest ISE. As a result, a performance curve of ACO that is as closed as to the set-point with high performances (lower overshoot value, shorter system dead time and settling time and lower ISE if compared to the conventional PID tuning methods) is obtained

Identifying hazardousness of sewer pipeline gas mixture using classification methods: a comparative study

In this work, we formulated a real-world problem related to sewer pipeline gas detection using the classification-based approaches. The primary goal of this work was to identify the hazardousness of sewer pipeline to offer safe and non-hazardous access to sewer pipeline workers so that the human fatalities, which occurs due to the toxic exposure of sewer gas components, can be avoided. The dataset acquired through laboratory tests, experiments, and various literature sources was organized to design a predictive model that was able to identify/classify hazardous and non-hazardous situation of sewer pipeline. To design such prediction model, several classification algorithms were used and their performances were evaluated and compared, both empirically and statistically, over the collected dataset. In addition, the performances of several ensemble methods were analyzed to understand the extent of improvement offered by these methods. The result of this comprehensive study showed that the instance-based learning algorithm performed better than many other algorithms such as multilayer perceptron, radial basis function network, support vector machine, reduced pruning tree. Similarly, it was observed that multi-scheme ensemble approach enhanced the performance of base predictors

Optimization of an Autonomous Mobile Robot Path Planning Based on Improved Genetic Algorithms

Mobile robots are intended to operate in a variety of environments, and they need to be able to navigate and travel around obstacles, such as objects and barriers. In order to guarantee that the robot will not come into contact with any obstacles or other objects during its movement, algorithms for path planning have been demonstrated. The basic goal while constructing a route is to find the fastest and smoothest route between the starting point and the destination. This article describes route planning using the improvised genetic algorithm with the Bezier Curve (GA-BZ). This study carried out two main experiments, each using a 20x20 random grid map model with varying percentages of obstacles (5%, 15%, and 30% in the first experiment, and 25% and 50% in the second). In the initial experiments, the population (PN), generation (GN), and mutation rate (MR) of genetic algorithms (GA) will be altered to the following values: (PN = 100, 125, 150, or 200; GN = 100, 125, 150; and MR = 0.1, 0.3, 0.5, 0.7) respectively. The goal is to evaluate the effectiveness of AMR in terms of travel distance (m), total time (s), and total cost (RM) in comparison to traditional GA and GA-BZ. The second experiment examined robot performance utilising GA, GA-BZ, Simulated Annealing (SA), A-Star (A*), and Dijkstra's Algorithms (DA) for path distance (m), time travel (s), and fare trip (RM). The simulation results are analysed, compared, and explained. In conclusion, the project is summarised

A generalized laser simulator algorithm for optimal path planning in constraints environment

Path planning plays a vital role in autonomous mobile robot navigation, and it has thus become one of the most studied areas in robotics. Path planning refers to a robot's search for a collision-free and optimal path from a start point to a predefined goal position in a given environment. This research focuses on developing a novel path planning algorithm, called Generalized Laser Simulator (GLS), to solve the path planning problem of mobile robots in a constrained environment. This approach allows finding the path for a mobile robot while avoiding obstacles, searching for a goal, considering some constraints and finding an optimal path during the robot movement in both known and unknown environments. The feasible path is determined between the start and goal positions by generating a wave of points in all directions towards the goal point with adhering to constraints. A simulation study employing the proposed approach is applied to the grid map settings to determine a collision-free path from the start to goal positions. First, the grid mapping of the robot's workspace environment is constructed, and then the borders of the workspace environment are detected based on the new proposed function. This function guides the robot to move toward the desired goal. Two concepts have been implemented to find the best candidate point to move next: minimum distance to goal and maximum index distance to the boundary, integrated by negative probability to sort out the most preferred point for the robot trajectory determination. In order to construct an optimal collision-free path, an optimization step was included to find out the minimum distance within the candidate points that have been determined by GLS while adhering to particular constraint's rules and avoiding obstacles. The proposed algorithm will switch its working pattern based on the goal minimum and boundary maximum index distances. For static obstacle avoidance, the boundaries of the obstacle(s) are considered borders of the environment. However, the algorithm detects obstacles as a new border in dynamic obstacles once it occurs in front of the GLS waves. The proposed method has been tested in several test environments with different degrees of complexity. Twenty different arbitrary environments are categorized into four: Simple, complex, narrow, and maze, with five test environments in each. The results demonstrated that the proposed method could generate an optimal collision-free path. Moreover, the proposed algorithm result are compared to some common algorithms such as the A* algorithm, Probabilistic Road Map, RRT, Bi-directional RRT, and Laser Simulator algorithm to demonstrate its effectiveness. The suggested algorithm outperforms the competition in terms of improving path cost, smoothness, and search time. A statistical test was used to demonstrate the efficiency of the proposed algorithm over the compared methods. The GLS is 7.8 and 5.5 times faster than A* and LS, respectively, generating a path 1.2 and 1.5 times shorter than A* and LS. The mean value of the path cost achieved by the proposed approach is 4% and 15% lower than PRM and RRT, respectively. The mean path cost generated by the LS algorithm, on the other hand, is 14% higher than that generated by the PRM. Finally, to verify the performance of the developed method for generating a collision-free path, experimental studies were carried out using an existing WMR platform in labs and roads. The experimental work investigates complete autonomous WMR path planning in the lab and road environments using live video streaming. The local maps were built using data from live video streaming s by real-time image processing to detect the segments of the lab and road environments. The image processing includes several operations to apply GLS on the prepared local map. The proposed algorithm generates the path within the prepared local map to find the path between start-to-goal positions to avoid obstacles and adhere to constraints. The experimental test shows that the proposed method can generate the shortest path and best smooth trajectory from start to goal points in comparison with the laser simulator

Path planning, modelling and simulation for energy optimised mobile robotics

This thesis is concerned with an investigation of a solution for mobile robotic platforms to minimize the usage of scarce energy that is available and is not wasted following traditionally planned paths for complex terrain environments. This therefore addresses the need to reduce the total energy cost during a field task or mission. A path planning algorithm is designed by creating a new approach of artificial potential field method that generates a planned path, utilising terrain map. The new approach has the capability of avoiding the local minimum problems which is one of the major problems of traditional potential field method. By solving such problems gives a reliable solution to establish a required path. Therefore the approach results in an energy efficient path of the terrain identified, instead obvious straight line of the terrain. A literature review is conducted which reviews the mainstream path planning algorithms with the applications in mobile robotic platforms was analysed. These path planning algorithms are compared for the purpose of energy optimized planning, which concludes the method of artificial potential field as the path planning algorithm which has the most potential and will be further investigated and improved in this research. The methodology of designing, modelling and simulating a mobile robotic platform is defined and presented for the purpose of energy optimized path planning requirement. The research is to clarify the needs, requirements, and specifications of the design. A complete set of models which include mechanical and electrical modelling, functional concept modelling, modelling of the system are established. Based on these models, an energy optimized path planning algorithm is designed. The modelling of force and the kinematics is established to validate and evaluate the result of the algorithm through simulations. Moreover a simulation environment is established which is constructed for multi perspective simulation. This also enables collaborative simulation using Simulink and ADAMS to for simulating a path generated by the path planning algorithm and assess the energy consumption of the driven and steering mechanism of an exemplar system called AgriRover. This simulation environment allows the capture of simulated result of the total energy consumption, therefore outlines the energy cost behaviour of the AgriRover. A total of two sets of paths was tested in the fields for validation, one being generated by the energy optimized path planning algorithm and the other following a straight path. During the field tests the total cost of energy was captured . Two sets of results are compared with each other and compared with the simulation. The comparison shows a 21.34% of the energy saving by deploying the path generated with the energy optimized path planning algorithm in the field test. This research made the following contribution to knowledge. A comparison and grading of mainstream path planning algorithms from energy optimisation perspective is undertaken using detailed evaluation criteria, including computational power required, extendibility, flexibility and more criteria that is relevant for the energy optimized planning purpose. These algorithms have not been compared from energy optimisation angle before, and the research for energy optimised planning under complex terrain environments have not been investigated. Addressing these knowledge gaps, a methodology of designing, modelling and simulating a mobile platform system is proposed to facilitate an energy optimized path planning. This , leads to a new approach of path planning algorithm that reduces unnecessary energy spend for climbing of the terrain, using the terrain data available. Such a methodology derives several novel methods: Namely, a method for avoiding local minimum problem for artificial potential field path planning using the approach of approximation; A method of achieving high expendability of the path planning algorithm, where this method is capable of generate a path through a large map in a short time; A novel method of multi perspective dynamic simulation, which is capable of simulating the behaviour of internal mechanism and the overall robotic mobile platform with the fully integrated control, The dynamic simulation enables prediction of energy consumption; Finally, a novel method of mathematically modelling and simplifying a steering mechanism for the wheel based mobile vehicle was further investigated.This thesis is concerned with an investigation of a solution for mobile robotic platforms to minimize the usage of scarce energy that is available and is not wasted following traditionally planned paths for complex terrain environments. This therefore addresses the need to reduce the total energy cost during a field task or mission. A path planning algorithm is designed by creating a new approach of artificial potential field method that generates a planned path, utilising terrain map. The new approach has the capability of avoiding the local minimum problems which is one of the major problems of traditional potential field method. By solving such problems gives a reliable solution to establish a required path. Therefore the approach results in an energy efficient path of the terrain identified, instead obvious straight line of the terrain. A literature review is conducted which reviews the mainstream path planning algorithms with the applications in mobile robotic platforms was analysed. These path planning algorithms are compared for the purpose of energy optimized planning, which concludes the method of artificial potential field as the path planning algorithm which has the most potential and will be further investigated and improved in this research. The methodology of designing, modelling and simulating a mobile robotic platform is defined and presented for the purpose of energy optimized path planning requirement. The research is to clarify the needs, requirements, and specifications of the design. A complete set of models which include mechanical and electrical modelling, functional concept modelling, modelling of the system are established. Based on these models, an energy optimized path planning algorithm is designed. The modelling of force and the kinematics is established to validate and evaluate the result of the algorithm through simulations. Moreover a simulation environment is established which is constructed for multi perspective simulation. This also enables collaborative simulation using Simulink and ADAMS to for simulating a path generated by the path planning algorithm and assess the energy consumption of the driven and steering mechanism of an exemplar system called AgriRover. This simulation environment allows the capture of simulated result of the total energy consumption, therefore outlines the energy cost behaviour of the AgriRover. A total of two sets of paths was tested in the fields for validation, one being generated by the energy optimized path planning algorithm and the other following a straight path. During the field tests the total cost of energy was captured . Two sets of results are compared with each other and compared with the simulation. The comparison shows a 21.34% of the energy saving by deploying the path generated with the energy optimized path planning algorithm in the field test. This research made the following contribution to knowledge. A comparison and grading of mainstream path planning algorithms from energy optimisation perspective is undertaken using detailed evaluation criteria, including computational power required, extendibility, flexibility and more criteria that is relevant for the energy optimized planning purpose. These algorithms have not been compared from energy optimisation angle before, and the research for energy optimised planning under complex terrain environments have not been investigated. Addressing these knowledge gaps, a methodology of designing, modelling and simulating a mobile platform system is proposed to facilitate an energy optimized path planning. This , leads to a new approach of path planning algorithm that reduces unnecessary energy spend for climbing of the terrain, using the terrain data available. Such a methodology derives several novel methods: Namely, a method for avoiding local minimum problem for artificial potential field path planning using the approach of approximation; A method of achieving high expendability of the path planning algorithm, where this method is capable of generate a path through a large map in a short time; A novel method of multi perspective dynamic simulation, which is capable of simulating the behaviour of internal mechanism and the overall robotic mobile platform with the fully integrated control, The dynamic simulation enables prediction of energy consumption; Finally, a novel method of mathematically modelling and simplifying a steering mechanism for the wheel based mobile vehicle was further investigated

Bridge structure deformation prediction based on GNSS data using Kalman-ARIMA-GARCH model

Bridges are an essential part of the ground transportation system. Health monitoring is fundamentally important for the safety and service life of bridges. A large amount of structural information is obtained from various sensors using sensing technology, and the data processing has become a challenging issue. To improve the prediction accuracy of bridge structure deformation based on data mining and to accurately evaluate the time-varying characteristics of bridge structure performance evolution, this paper proposes a new method for bridge structure deformation prediction, which integrates the Kalman filter, autoregressive integrated moving average model (ARIMA), and generalized autoregressive conditional heteroskedasticity (GARCH). Firstly, the raw deformation data is directly pre-processed using the Kalman filter to reduce the noise. After that, the linear recursive ARIMA model is established to analyze and predict the structure deformation. Finally, the nonlinear recursive GARCH model is introduced to further improve the accuracy of the prediction. Simulation results based on measured sensor data from the Global Navigation Satellite System (GNSS) deformation monitoring system demonstrated that: (1) the Kalman filter is capable of denoising the bridge deformation monitoring data; (2) the prediction accuracy of the proposed Kalman-ARIMA-GARCH model is satisfactory, where the mean absolute error increases only from 3.402 mm to 5.847 mm with the increment of the prediction step; and (3) in comparision to the Kalman-ARIMA model, the Kalman-ARIMA-GARCH model results in superior prediction accuracy as it includes partial nonlinear characteristics (heteroscedasticity); the mean absolute error of five-step prediction using the proposed model is improved by 10.12%. This paper provides a new way for structural behavior prediction based on data processing, which can lay a foundation for the early warning of bridge health monitoring system based on sensor data using sensing technolog