A Case based Online Trajectory Planning Method of Autonomous Unmanned Combat Aerial Vehicles with Weapon Release Constraints

As a challenging and highly complex problem, the trajectory planning for unmanned combat aerial vehicle (UCAV) focuses on optimising flight trajectory under such constraints as kinematics and complicated battlefield environment. An online case-based trajectory planning strategy is proposed in this study to achieve rapid control variables solution of UCAV flight trajectory for the of delivery airborne guided bombs. Firstly, with an analysis of the ballistic model of airborne guided bombs, the trajectory planning model of UCAVs is established with launch acceptable region (LAR) as a terminal constraint. Secondly, a case-based planning strategy is presented, which involves four cases depending on the situation of UCAVs at the current moment. Finally, the feasibility and efficiency of the proposed planning strategy is validated by numerical simulations, and the results show that the presented strategy is suitable for UCAV performing airborne guided delivery missions in dynamic environments

Multi-criteria Evolution of Neural Network Topologies: Balancing Experience and Performance in Autonomous Systems

Majority of Artificial Neural Network (ANN) implementations in autonomous systems use a fixed/user-prescribed network topology, leading to sub-optimal performance and low portability. The existing neuro-evolution of augmenting topology or NEAT paradigm offers a powerful alternative by allowing the network topology and the connection weights to be simultaneously optimized through an evolutionary process. However, most NEAT implementations allow the consideration of only a single objective. There also persists the question of how to tractably introduce topological diversification that mitigates overfitting to training scenarios. To address these gaps, this paper develops a multi-objective neuro-evolution algorithm. While adopting the basic elements of NEAT, important modifications are made to the selection, speciation, and mutation processes. With the backdrop of small-robot path-planning applications, an experience-gain criterion is derived to encapsulate the amount of diverse local environment encountered by the system. This criterion facilitates the evolution of genes that support exploration, thereby seeking to generalize from a smaller set of mission scenarios than possible with performance maximization alone. The effectiveness of the single-objective (optimizing performance) and the multi-objective (optimizing performance and experience-gain) neuro-evolution approaches are evaluated on two different small-robot cases, with ANNs obtained by the multi-objective optimization observed to provide superior performance in unseen scenarios

Minimum Distance and Minimum Time Optimal Path Planning with Bioinspired Machine Learning Algorithms for Impaired Unmanned Air Vehicles

Unmanned air vehicles operate in highly dynamic and unknown environments where they can encounter unexpected and unseen failures. In the presence of emergencies, autonomous unmanned air vehicles should be able to land at a minimum distance or minimum time. Impaired unmanned air vehicles define actuator failures and this impairment changes their unstable and uncertain dynamics; henceforth, path planning algorithms must be adaptive and model-free. In addition, path planning optimization problems must consider the unavoidable actuator saturations, kinematic and dynamic constraints for successful real-time applications. Therefore, this paper develops 3D path planning algorithms for quadrotors with parametric uncertainties and various constraints. In this respect, this paper constructs a multi-dimensional particle swarm optimization and a multi-dimensional genetic algorithm to plan paths for translational, rotational, and Euler angles and generates the corresponding control signals. The algorithms are assessed and compared both in the simulation and experimental environments. Results show that the multi-dimensional genetic algorithm produces shorter minimum distance and minimum time paths under the constraints. The real-time experiments prove that the quadrotor exactly follows the produced path utilizing the available maximum rotor speeds

Extending the solid step fixed-charge transportation problem to consider two-stage networks and multi-item shipments

This paper develops a new mathematical model for a capacitated solid step fixed-charge transportation problem. The problem is formulated as a two-stage transportation network and considers the option of shipping multiple items from the plants to the distribution centers (DC) and afterwards from DCs to customers. In order to tackle such an NP-hard problem, we propose two meta-heuristic algorithms; namely, Simulated Annealing (SA) and Imperialist Competitive Algorithm (ICA). Contrary to the previous studies, new neighborhood strategies maintaining the feasibility of the problem are developed. Additionally, the Taguchi method is used to tune the parameters of the algorithms. In order to validate and evaluate the performances of the model and algorithms, the results of the proposed SA and ICA are compared. The computational results show that the proposed algorithms provide relatively good solutions in a reasonable amount of time. Furthermore, the related comparison reveals that the ICA generates superior solutions compared to the ones obtained by the SA algorithm

Assessment of machinability of inconel 718: A comparative study of CVD & PVD coated tools

281-297This paper highlights the parametric appraisal in turning of inconel 718 using fuzzy inference system coupled with imperialistic competitive algorithm (ICA) approach. The machining variables such as spindle speed, feed rate and depth of cut have been taken into consideration to analyse their effect on evaluation characteristics viz. material removal rate (MRR), flank wear and surface roughness. Fuzzy inference system (FIS) has been used to integrate aforementioned evaluation characteristics into a single response known as multi performance characteristic index (MPCI) to address the issue of impreciseness and uncertainties involved in decision making. Mathematical models have also been proposed for MPCI using non-linear regression analysis which acts as an objective function in ICA. ICA is new meta-heuristic based on social political theory which is used to obtain global optimal parametric combination in machining of Inconel 718. The results indicate that single layer (single coating: AlTiN) physical vapour deposition (PVD) coated tool is more efficient as compared to multi-layered (four coatings: TiN, TiCN, Al2O3 and TiN) chemical vapour deposition (CVD) coated tool

Land Suitability Analysis as Multi Criteria Decision Making to Support the Egyptian Urban Development

Sustainability in urban development is considered as a main concrete stone that effect directly the quality of life for its users. Land Suitability Analysis (LSA) using GIS as a multi criteria support tool reveals the best alternatives for the suitability of sustainable land development. Urban planners working under the umbrella of sustainability using recent technology should contribute their work directly to LSA. This paper aims to develop a new technique to be used by planner to reach best alternative for five main urban sectors (agriculture, Industry, Trade, Tourism, & Residential) using GIS as a multi criteria decision support tool (MCDS), accordingly choosing best city location will be accurately and analyzed upon LSA studies. LSA and MCDS are going to be applied on one survey unit map called Monof along Cairo – Alexandria Road. Results showed that different alternatives could be applied on the area of interest, and all of them are sustainable, but choosing the best deepened on the priority of querying the development sector. The paper suggests a pilot method for land development planning and choosing best city location that would be a guide for the governmental planning organization to support in taking right and analyzed planning decisions

An experimental and simulation study on parametric analysis in turning of inconel 718 and GFRP composite using coated and uncoated tools

Process simulation is one of the important aspects in any manufacturing/production context because it generates the scenarios to gain insight into process performance in reasonable time and cost. With upcoming worldwide applications of Inconel 718 and Glass Fiber Reinforced Polymer (GFRP) composites, machining has become an important issue which needs to be investigated in detail. In turning of hard materials (such as Inconel 718), cutting tool environment features high-localized temperatures (~1000ºC) and high stress (~700 MPa) due to contact between cutting tool and work piece. The tool may experience repeated impact loads during interrupted cuts and the work piece chips may chemically interact with the tool materials. Therefore, the use of coated tool is preferred for turning of Inconel 718. It is observed that performance of machining process is influenced by different machining parameters such as spindle speed, depth of cut and feed rate as in case of turning. Material removal rate (MRR) and flank wear in turning of Inconel 718 using physical vapour deposition (PVD) and chemical vapour deposition (CVD) coated on carbide insert tool are reported. A simulation model based on finite element approach is proposed using DEFORM 3D software. The simulation results are validated with experimental results. The results indicate that simulation model can be effectively used to predict the flank wear and MRR in turning of Inconel 718. For simultaneous optimization of multiple responses, a fuzzy inference system (FIS) is used to convert multiple responses into a single equivalent response so that uncertainty and fuzziness in data can be addressed in an effective manner. The single response characteristics so generated is known as Multi Performance characteristic Index (MPCI). A non-linear empirical model has been developed using regression analysis between MPCI and process parameters. The optimal process parameters are obtained by a recent population-based optimization method known as imperialistic competitive algorithm (ICA). Analysis of variance (ANOVA) is performed to identify the most influencing factors for all the performance characteristics. The optimal conditions of process parameters during turning of Inconel 718 and GFRP composites are reported. It is observed that flank wear is combatively less when machined with PVD coated tool than CVD coated tool in turning of both Inconel 718 and GFRP composite

A Cluster-Based Opposition Differential Evolution Algorithm Boosted by a Local Search for ECG Signal Classification

Electrocardiogram (ECG) signals, which capture the heart's electrical activity, are used to diagnose and monitor cardiac problems. The accurate classification of ECG signals, particularly for distinguishing among various types of arrhythmias and myocardial infarctions, is crucial for the early detection and treatment of heart-related diseases. This paper proposes a novel approach based on an improved differential evolution (DE) algorithm for ECG signal classification for enhancing the performance. In the initial stages of our approach, the preprocessing step is followed by the extraction of several significant features from the ECG signals. These extracted features are then provided as inputs to an enhanced multi-layer perceptron (MLP). While MLPs are still widely used for ECG signal classification, using gradient-based training methods, the most widely used algorithm for the training process, has significant disadvantages, such as the possibility of being stuck in local optimums. This paper employs an enhanced differential evolution (DE) algorithm for the training process as one of the most effective population-based algorithms. To this end, we improved DE based on a clustering-based strategy, opposition-based learning, and a local search. Clustering-based strategies can act as crossover operators, while the goal of the opposition operator is to improve the exploration of the DE algorithm. The weights and biases found by the improved DE algorithm are then fed into six gradient-based local search algorithms. In other words, the weights found by the DE are employed as an initialization point. Therefore, we introduced six different algorithms for the training process (in terms of different local search algorithms). In an extensive set of experiments, we showed that our proposed training algorithm could provide better results than the conventional training algorithms.Comment: 44 pages, 9 figure

Reactive evolutionary path planning for autonomous surface vehicles in lake environments.

Autonomous Surface Vehicles (ASVs) have found a lot of promising applications in aquatic environments, i.e., sea, lakes, rivers, etc. They can be used for applications of paramount importance, such as environmental monitoring of water resources, and for bathymetry to study the characteristics of the basing of a lake/sea or for surveillance in patrol missions, among others. These vehicles can be built with smaller dimensions when compared to regular ships since they do not need an on-board crew for operation. However, they do require at least a telemetry control as well as certain intelligence for making decisions and responding to changing scenarios. Water resources are very important in Paraguay since they provide fresh water for its inhabitants and they are crucial for the main economic activities such as agriculture and cattle raising. Furthermore, they are natural borders with the surrounding countries, and consequently the main transportation route for importing/exporting products. In fact, Paraguay is the third country in the world with the largest fleet of barges after USA and China. Thus, maintaining and monitoring the environmental conditions of these resources is key in the development of the country. This work is focused on the maintenance and monitoring of the greatest lake of the country called Ypacarai Lake. In recent years, the quality of its water has been seriously degraded due to the pollution caused by the low control of the dumping of waste thrown into the Lake. Since it is also a national icon, the government of Paraguay has put a lot of effort in recovering water quality of the Lake. As a result, it is monitored periodically but using manual procedures. Therefore, the primary objective of this work is to develop these monitoring tasks autonomously by means of an ASV with a suitable path planning strategy. Path planning is an active research area in robotics. A particular case is the Coverage Path Planning (CPP) problem, where an algorithm should find a path that achieves the best coverage of the target region to be monitored. This work mainly studies the global CPP, which returns a suitable path considering the initial conditions of the environment where the vehicle moves. The first contribution of this thesis is the modeling of the CPP using Hamiltonian Circuits (HCs) and Eulerian Circuits (ECs). Therefore, a graph adapted to the Ypacarai Lake is created by using a network of wireless beacons located at the shore of the lake, so that they can be used as data exchange points between a control center and the ASV, and also as waypoints. Regarding the proposed modeling, HCs and ECs are paths that begin and end at the same point. Therefore, the ASV travels across a given graph that is defined by a set of wireless beacons. The main difference between HC and EC is that a HC is a tour that visits each vertex only once while EC visits each edge only once. Finding optimal HCs or ECs that minimize the total distance traveled by the ASV are very complex problems known as NP-complete. To solve such problems, a meta-heuristic algorithm can be a suitable approach since they provide quasi-optimal solutions in a reasonable time. In this work, a GA (Genetic Algorithm) approach is proposed and tested. First, an evaluation of the performance of the algorithm with different values of its hyper-parameters has been carried out. Second, the proposed approach has been compared to other approaches such as randomized and greedy algorithms. Third, a thorough comparison between the performance of HC and EC based approaches is presented. The simulation results show that EC-based approach outperforms the HC-based approach almost 2% which in terms of the Lake size is about 1.4 km2 or 140 ha (hectares). Therefore, it has been demonstrated that the modeling of the problem as an Eulerian graph provides better results. Furthermore, it has been investigated the relationship between the number of beacons to be visited and the distance traveled by the ASV in the EC-based approach. Findings indicate that there is a quasi-lineal relationship between the number of beacons and the distance traveled. The second contribution of this work is the development of an on-line learning strategy using the same model but considering dynamic contamination events in the Lake. Dynamic events mean the appearance and evolution of an algae bloom, which is a strong indicator of the degradation of the lake. The strategy is divided into two-phases, the initial exploration phase to discover the presence of the algae bloom and next the intensification phase to focus on the region where the contamination event is detected. This intensification effect is achieved by modifying the beacon-based graph, reducing the number of vertices and selecting those that are closer to the region of interest. The simulation results reveal that the proposed strategy detects two events and monitors them, keeping a high level of coverage while minimizing the distance traveled by the ASV. The proposed scheme is a reactive path planning that adapts to the environmental conditions. This scheme makes decisions in an autonomous way and it switches from the exploratory phase to the intensification phase depending on the external conditions, leading to a variable granularity in the monitoring task. Therefore, there is a balance between coverage and the energy consumed by the ASV. The main benefits obtained from the second contribution includes a better monitoring in the quality of water and control of waste dumping, and the possibility to predict the appearance of algae-bloom from the collected environmental data