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Hybrid systems are a group of dynamical system which their behavior described by the interaction of discrete and continuous dynamical system behaviors. One of the subsets of hybrid systems, is piecewise affine system. Piecewise affine system identification, consists of estimating the parameters of each subsystem and the coefficients of the state-input boundary hyperplanes. In order to clustering the state-input space and estimating the feature matrixes simultaneously, bounded error algorithm and adaline neural network are used. It should be said that in this method, there is no need to know the number of linear subsystems of the piecewise affine system. Moreover, it should be noted that the identification method is extended based on on-line data acquisition from system. In continuation, this method is used to identify a benchmark mathematical piecewise affine system. By comparing the results with the reference paper, it is proven that this method has a good performance in clustering the state-input space and estimating the feature matrixes. In the end, by using the proposed method, an active water tank which its equations are described by the form of a piecewise affine system is identified.

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Cooperation and autonomy are among the most important aspects of unmanned systems through which greater use of these system is possible. Most applications in civil market is related to government organizations requiring surveillance and inspection, such as coast guards, border patrol, emergency services and police. A cooperation algorithm is developed and simulated in this research for autonomous UAVs to track a dynamic target in an adversarial environment. First, a mathematical formulation is developed to represent the area of operation that contains various types of threats in a single framework. Then a search point guidance algorithm is developed by using a rule-based approach to guide every UAV to the way points created by the cooperation algorithm, with the requirements of completing mission, avoiding restricted areas, minimizing threat exposure level, considering the dynamic constraints of the UAVs and avoiding collision. The cooperation algorithm is designed based on a variable formation which depends on a cost function. The efficiency of the team is improved in the terms of increasing the area of coverage of the sensors, flexibility of the UAVs to search for better trajectories in terms of restricted area avoidance and threat exposure minimization, and improving the estimation. Finally, the performance of the algorithm is evaluated in a MATLAB environment, which includes the dynamics of vehicles, the models of sensor measurement and data communication and the discrete execution of the algorithms. The simulation results demonstrate that the proposed algorithms successfully generated the trajectories that satisfy the given mission objectives.

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Flight schedule design and fleet assignment are the main sub problems of the airline schedule planning which have the most effect on the costs and profit of the airline. In this paper, integrated flight schedule design and fleet assignment problem is described and genetic algorithm has been developed to solve this problem. It has numbers of constraints and multi-layer permutation chromosomes with variable length. So, creating the initial population randomly and use of customary operators of evolutionary algorithms will not be efficient since the probability of feasibility is very low. For this purpose, a new function based on loop concept to create an initial population and new crossover and mutation operators have been developed. A genetic algorithm has been used within the main loop to optimize the redirection of the passengers. Four models with different numbers of airports and fleets are created as an input for the problem which have been solved by two and three islands genetic algorithms. Results show that in each iteration of the main loop, feasible answers are obtained and finally there was a proper improvement in the costs. In larger models, there is a better Improvement in the costs and more difference between two and three islands algorithms. Three islands mode results in a better solution within a longer time. The developed algorithm can successfully find feasible optimal solution and it can be used for high-dimensional problems in which there is no possibility to find the optimal solution by using conventional methods such as MILP.

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Multi-robot path planning problem involves some challenges. One of them is the exponential increase in the size of the search space as a result of increasing the number of robots in the operating environment. Therefore, there is a need for algorithms with high computational performance to plan optimal and collision-free paths in a limited time. In this article, a centralized path planning algorithm is presented. The algorithm is a heuristic incremental search, in which the D* Lite algorithm has been adapted for the multi-robot case. The concept of occupancy time has been embedded into the environment model to avoid path interference. A centralized function has been designed to update the environment model and robot data. To evaluate the method, two groups of simulations of static and dynamic types were carried out. The static simulations focused on studying the effect of algorithm parameters, and it was shown that the algorithm can plan paths for up to 40 robots in an environment having 55 percent free space. The dynamic simulations were carried out in Gazebo, a real-time and dynamic physical simulator. The results were compared to a baseline method based on potential fields. The number of robots was increased to 14, and it was demonstrated that for 9 robots and more, the potential field approach either fails or has a rapid increase in computation time, while the proposed method can find feasible solutions in a limited time.

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The Artificial Potential Fields approach is amongst the widely used path planning methods in continuous environments. However, the implementation of it in multi-robot path planning encounters challenges such as the local-minima and an increase in traffic probability with the rise in the number of robots. The purpose of the proposed method is to improve multi-robot path planning in complex environments. A new adaptive potential function is introduced that reduces the probability of the robots entering an area at the same time and thus reducing the probability of traffic. Also, new potential functions have been proposed that lead to smoother paths with less traverse time when the robot encounters obstacles. In these functions, in addition to the position of robots and obstacles, heading of the robot and the position of the target are also considered. In order to evaluate this method, a distributed software architecture has been designed and implemented in the framework of the robot operating system. In this architecture, as robots move, new robots can join the operation or new tasks can be assigned to robots. Two series of real-time simulations are carried out in the Gazebo environment. The results show that the use of the proposed potential functions leads to a decrease in the convergence of the robots. In the simulation done for 2 robots, proposed method has resulted in a 35% reduction in the traversal time. While in case of 15 robots in the same map, a 50% reduction in the traversal time has been achieved.