Verifying collision avoidance behaviours for unmanned surface vehicles using probabilistic model checking

Collision avoidance is an essential safety requirement for unmanned surface vehicles (USVs). Normally, its practical verification is non-trivial, due to the stochastic behaviours of both the USVs and the intruders. This paper presents the probabilistic timed automata (PTAs) based formalism for three collision avoidance behaviours of USVs in uncertain dynamic environments, which are associated with the crossing situation in COLREGs. Steering right, acceleration, and deceleration are considered potential evasive manoeuvres. The state-of-the-art prism model checker is applied to analyse the underlying models. This work provides a framework and practical application of the probabilistic model checking for decision making in collision avoidance for USVs

The interactive design approach for aerodynamic shape design optimisation of the Aegis UAV

In this work, an interactive optimisation framework—a combination of a low fidelity flow solver, Athena Vortex Lattice (AVL), and an interactive Multi-Objective Particle Swarm Optimisation (MOPSO)—is proposed for aerodynamic shape design optimisation of any aerial vehicle platform. This paper demonstrates the benefits of interactive optimisation—reduction of computational time with high optimality levels. Progress towards the most preferred solutions is made by having the Decision Maker (DM) periodically provide preference information once the MOPSO iterations are underway. By involving the DM within the optimisation process, the search is directed to the region of interest, which accelerates the process. The flexibility and eciency of undertaking optimisation interactively have been demonstrated by comparing the interactive results with the non-interactive results of an optimum design case obtained using Multi-Objective Tabu Search (MOTS) for the Aegis UAV. The obtained results show the superiority of using an interactive approach for the aerodynamic shape design, compared to posteriori approaches. By carrying out the optimisation using interactive MOPSO it was shown to be possible to obtain similar results to non-interactive MOTS with only half the evaluations. Moreover, much of the usual complexity of post-data-analysis with posteriori approaches is avoided, since the DM is involved in the search process

Initial investigation of aerodynamic shape design optimisation for the Aegis UAV

This paper presents an aerodynamic design optimisation methodology used in further developing an already existing Unmanned Aerial Vehicle (UAV) platform called Aegis. This paper aims to deliver a medium altitude long endurance UAV for civilian purposes. The methodology used is also applicable to conceptual and preliminary design phases of any aerial vehicle platform. It combines a low fidelity aerodynamic analysis tool, Athena Vortex Lattice Code, with a design optimisation tool (Nimrod/O). The meta-heuristic algorithm, Multi-Objective Tabu Search-2 (MOTS2), is used to perform the optimisation process. This new methodological study optimises the UAV wing planform for level flight. It was used successfully to obtain a set of optimal wing shapes for the Aegis UAV flying at different speeds. Prior to the formulation of the design problem, a parametric study was performed to explore the design space and provide an insight into how the objective functions behave with respect to the design variables. The methodology presented here is not finalized, it is a first step to constructing a general framework that can be used to optimise the design of a twin-boom UAV aerodynamic shape. The interfacing of the already successful packages Nimrod/O, MOTS2, and AVL software produces an initial result that shows the capability of the new methodology to provide correct support decisions making for a design optimisation process that will benefit the entire community of UAV researchers and designers when it is complete

Fuzzy physical programming for Space Manoeuvre Vehicles trajectory optimization based on hp-adaptive pseudospectral method

In this paper, a fuzzy physical programming (FPP) method has been introduced for solving multi-objective Space Manoeuvre Vehicles (SMV) skip trajectory optimization problem based on hp-adaptive pseudospectral methods. The dynamic model of SMV is elaborated and then, by employing hp-adaptive pseudospectral methods, the problem has been transformed to nonlinear programming (NLP) problem. According to the mission requirements, the solutions were calculated for each single-objective scenario. To get a compromised solution for each target, the fuzzy physical programming (FPP) model is proposed. The preference function is established with considering the fuzzy factor of the system such that a proper compromised trajectory can be acquired. In addition, the NSGA-II is tested to obtain the Pareto-optimal solution set and verify the Pareto optimality of the FPP solution. Simulation results indicate that the proposed method is effective and feasible in terms of dealing with the multi-objective skip trajectory optimization for the SMV

Fuzzy logic based equivalent consumption optimization of a hybrid electric propulsion system for unmanned aerial vehicles

This paper presents an energy management strategy for a hybrid electric propulsion system designed for unmanned aerial vehicles. The proposed method combines the Equivalent Consumption Minimization Strategy (ECMS) and fuzzy logic control, thereby being named Fuzzy based ECMS (F-ECMS). F-ECMS can solve the issue that the conventional ECMS cannot sustain the battery state-of-charge for on-line applications. Furthermore, F-ECMS considers the aircraft safety and guarantees the aircraft landing using the remaining electrical energy if the engine fails. The main contribution of the paper is to solve the deficiencies of ECMS and take into consideration the aircraft safely landing, by implementing F-ECMS. Compared with the combustion propulsion system, the hybrid propulsion system with F-ECMS at least reduces 11% fuel consumption for designed flight missions. The advantages of F-ECMS are further investigated by comparison with the conventional ECMS, dynamic programming and adaptive ECMS. In contrast with ECMS and dynamic programming, F-ECMS can accomplish a balance between sustaining the battery state-of-charge and electric energy consumption. F-ECMS is also superior to the adaptive ECMS because there are less fuel consumption and lower computational cost

Artificial intelligence to enhance aerodynamic shape optimisation of the Aegis UAV

This article presents an optimisation framework that uses stochastic multi-objective optimisation, combined with an Artificial Neural Network (ANN), and describes its application to the aerodynamic design of aircraft shapes. The framework uses the Multi-Objective Particle Swarm Optimisation (MOPSO) algorithm and the obtained results confirm that the proposed technique provides highly optimal solutions in less computational time than other approaches to the same design problem. The main idea was to focus computational effort on worthwhile design solutions rather than exploring and evaluating all possible solutions in the design space. It is shown that the number of valid solutions obtained using ANN-MOPSO compared to MOPSO for 3000 evaluations grew from 529 to 1006 (90% improvement) with a penalty of only 8.3% (11 min) in computational time. It is demonstrated that including an ANN, the ANN-MOPSO with 3000 evaluations produced a larger number of valid solutions than the MOPSO with 5500 evaluations, and in 33% less computational time (64 min). This is taken as confirmation of the potential power of ANNs when applied to this type of design problem

Sizing of hybrid electric propulsion system for retrofitting a mid-scale aircraft using non-dominated sorting genetic algorithm

The paper presents the sizing of a hybrid electric propulsion system for a prototype aircraft. The main contribution of the paper is to apply multi-objective optimization in the retrofit of a mid-scale aircraft and investigate the fuel economy of the hybrid aircraft for a particular mission cycle. Using the Non-dominated Sorting Genetic Algorithm (NSGA), the fuel consumptions for different flight durations are minimized, which represent the optimal trade-off between fuel consumption and flight duration. With no compromise on endurance and range, the maximum fuel reduction of the retrofitted hybrid aircraft reaches 17.6%, by comparison with the prototype aircraft. The retrofitted aircraft achieves better cruising and climbing performance with the sized hybrid propulsion system. The novelty of the study is the proposal of a new non-dominated sorting algorithm—Benchmark based Non-Dominated Sort (BNDS) for the NSGA. BNDS can reduce the number of comparisons and the time complexity of the non-dominated sorting process. A constraint handling approach is also integrated into the BNDS/NSGA to address performance and mission requirements

Analysis of optimization strategies for solving space manoeuvre vehicle trajectory optimization problem

In this paper, two types of optimization strategies are applied to solve the Space Manoeuvre Vehicle (SMV) trajectory optimization problem. The SMV dynamic model is constructed and discretized applying direct multiple shooting method. To solve the resulting Nonlinear Programming (NLP) problem, gradient-based and derivative free optimization techniques are used to calculate the optimal time history with respect to the states and controls. Simulation results indicate that the proposed strategies are effective and can provide feasible solutions for solving the constrained SMV trajectory design problem

A comparison between guidance laws for AUVs using relative kinematics

This paper presents a comparison between three popular guidance laws for path following of autonomous underwater vehicles: switching enclosure-based Line-Of-Sight (LOS), lookahead-based LOS, and vector field guidance laws. The equations of motion employ the concept of the relative kinematics, and a nonlinear controller is applied together with the guidance systems during path-following. The optimal tuning values for each guidance are selected using the Pareto efficiencies from multiple simulations in terms of providing low cross-track error and control effort. Performance analysis are carried out for a waypoint following scenario both with and without significant constant and irrotational ocean currents as disturbances. Simulation results are also presented using the model of an AUV

Violation learning differential evolution-based hp-Adaptive pseudospectral method for trajectory optimization of Space Maneuver Vehicle

The sensitivity of the initial guess in terms of optimizer based on hp-adaptive pseudospectral method for solving Space Maneuver Vehicles (SMV) trajectory optimization problem has long been recognised as a difficult problem. Because of the sensitivity with regard to the initial guess, it may cost the solver a large amount of time to do the Newton iteration and get the optimal solution or even the local optimal solution. In this paper, to provide the optimizer a better initial guess and solve the SMV trajectory optimization problem, an initial guess generator using violation learning deferential evolution algorithm is introduced. A new constraint-handling strategy without using penalty function is presented to modify the fitness values so that the performance of each candidate can be generalized. In addition, a learning strategy is designed to add diversity for the population in order to improve the convergency speed and avoid local optima. Several simulation results are conducted by using the combination algorithm; Simulation results indicated that using limited computational efforts, the method proposed to generate initial guess can have better performance in terms of convergency ability and convergency speed compared with other approaches. By using the initial guess, the combinational method can also enhance the quality of the solution and reduce the number of Newton iteration and computational time. Therefore, The method is potentially feasible for solving the SMV trajectory optimization problem