Verification of monocular and binocular pose estimation algorithms in vision-based UAVs autonomous aerial refueling system

With the high focus on autonomous aerial refueling(AAR), it becomes increasingly urgent to design efficient methods or algorithms for solving the AAR problems in complicated aerial environments. A vision-based technology for AAR is developed in this paper, and five monocular and binocular visual algorithms for pose estimation of the unmanned aerial vehicles(UAVs) are adopted and verified in this AAR system. The real-time on-board vision system is also designed for precise navigation in the UAVs docking phase. A series of out-door comparative experiments for different pose estimation algorithms are conducted to verify the feasibility and accuracy of the vision algorithms in AAR.

Robust Attitude Control for Reusable Launch Vehicles Based on Fractional Calculus and Pigeon-inspired Optimization

In this paper, a robust attitude control system based on fractional order sliding mode control and dynamic inversion approach is presented for the reusable launch vehicle(RLV)during the reentry phase. By introducing the fractional order sliding surface to replace the integer order one, we design robust outer loop controller to compensate the error introduced by inner loop controller designed by dynamic inversion approach. To take the uncertainties of aerodynamic parameters into account,stochastic robustness design approach based on the Monte Carlo simulation and Pigeon-inspired optimization is established to increase the robustness of the controller. Some simulation results are given out which indicate the reliability and effectiveness of the attitude control system.

Multi-objective pigeon-inspired optimization for brushless direct current motor parameter design

Pigeon-inspired optimization(PIO) is a new swarm intelligence optimization algorithm, which is inspired by the behavior of homing pigeons. A variant of pigeon-inspired optimization named multi-objective pigeon-inspired optimization(MPIO) is proposed in this paper. It is also adopted to solve the multi-objective optimization problems in designing the parameters of brushless direct current motors, which has two objective variables, five design variables, and five constraint variables. Furthermore, comparative experimental results with the modified non-dominated sorting genetic algorithm are given to show the feasibility, validity and superiority of our proposed MIPO algorithm.

Markov decision evolutionary game theoretic learning for cooperative sensing of unmanned aerial vehicles

As one of the major contributions of biology to competitive decision making,evolutionary game theory provides a useful tool for studying the evolution of cooperation.To achieve the optimal solution for unmanned aerial vehicles(UAVs) that are carrying out a sensing task,this paper presents a Markov decision evolutionary game(MDEG) based learning algorithm.Each individual in the algorithm follows a Markov decision strategy to maximize its payoff against the well known Tit-for-Tat strategy.Simulation results demonstrate that the MDEG theory based approach effectively improves the collective payoff of the team.The proposed algorithm can not only obtain the best action sequence but also a sub-optimal Markov policy that is independent of the game duration.Furthermore,the paper also studies the emergence of cooperation in the evolution of self-regarded UAVs.The results show that it is the adaptive ability of the MDEG based approach as well as the perfect balance between revenge and forgiveness of the Tit-for-Tat strategy that the emergence of cooperation should be attributed to.

Hybrid membrane computing and pigeon-inspired optimization algorithm for brushless direct current motor parameter design

In this paper, a novel approach is proposed for solving the parameter design problem of brushless direct current(BLDC) motor, which is based on the membrane computing(MC) and pigeon-inspired optimization(PIO) algorithm. The motor parameter design problem is converted to an optimization problem with five design parameters and six constraints. The PIO algorithm is introduced into the framework of MC for improving the global convergence performance. The hybrid algorithm can improve the population diversity with better searching efficiency. Comparative simulations are conducted, and comparative results are given to show the feasibility and effectiveness of our proposed hybrid algorithm for high nonlinear optimization problems.