Ground threat prediction-based path planning of unmanned autonomous helicopter using hybrid enhanced artificial bee colony algorithm

Unmanned autonomous helicopter(UAH)path planning problem is an important component of the UAH mission planning system.Aiming to reduce the influence of non-complete ground threat information on UAH path planning,a ground threat prediction-based path planning method is proposed based on artificial bee colony(ABC)algorithm by collaborative thinking strategy.Firstly,a dynamic threat distribution probability model is developed based on the characteristics of typical ground threats.The dynamic no-fly zone of the UAH is simulated and established by calculating the distribution probability of ground threats in real time.Then,a dynamic path planning method for UAH is designed in complex environment based on the real-time prediction of ground threats.By adding the collision warning mechanism to the path planning model,the flight path could be dynamically adjusted according to changing no-fly zones.Furthermore,a hybrid enhanced ABC algorithm is proposed based on collaborative thinking strategy.The proposed algorithm applies the leader-member thinking mechanism to guide the direction of population evolution,and reduces the negative impact of local optimal solutions caused by collaborative learning update strategy,which makes the optimization performance of ABC algorithm more controllable and efficient.Finally,simulation results verify the feasibility and effectiveness of the proposed ground threat prediction path planning method.

Evaluating the influence of 6 MV and 15 MV photon beams on prostate intensity-modulated radiation therapy plans

Objective We aimed to determine the ef ects of low- and high-energy intensity-modulated radiation therapy （IMRT） photon beams on the target volume planning and on the critical organs in the case of prostate can-cer. Methods Thirty plans were generated by using either 6 MV or 15 MV beams separately, and a combination of both 6 and 15 MV beams. Al plans were generated by using suitable planning objectives and dose con-straints, which were identical across the plans, except the beam energy. The plans were analyzed in terms of their target coverage, conformity, and homogeneity, regardless of the beam energy. Results The mean percentage values of V70 Gy for the rectal wal for the plans with 6 MV, 15 MV, and mixed-energy beams were 16.9%, 17.8%, and 16.4%, respectively, while the mean percentage values of V40 Gy were 53.6%, 52.3%, and 50.4%. The mean dose values to the femoral heads for the 6 MV, 15 MV, and mixed-en-ergy plans were 30.1 Gy, 25.5 Gy, and 25.4 Gy, respectively. The mean integral dose for the 6 MV plans was 10% larger than those for the 15 MV and mixed-energy plans.Conclusion These preliminary results suggest that mixed-energy IMRT plans may be advantageous with respect to the dosimetric characteristics of low- and high-energy beams. Although the reduction of dose to the organs at risk may not be clinical y relevant, in this study, IMRT plans using mixed-energy beams exhibited better OAR sparing and overal higher plan quality for deep-seated tumors.

HYBRID MULTI-OBJECTIVE GRADIENT ALGORITHM FOR INVERSE PLANNING OF IMRT

The intelligent optimization of a multi-objective evolutionary algorithm is combined with a gradient algorithm. The hybrid multi-objective gradient algorithm is framed by the real number. Test functions are used to analyze the efficiency of the algorithm. In the simulation case of the water phantom, the algorithm is applied to an inverse planning process of intensity modulated radiation treatment （IMRT）. The objective functions of planning target volume （PTV） and normal tissue （NT） are based on the average dose distribution. The obtained intensity profile shows that the hybrid multi-objective gradient algorithm saves the computational time and has good accuracy, thus meeting the requirements of practical applications.

A Hybrid Programming Model for Optimal Production Planning under Demand Uncertainty in Refinery

Abstract Production planning under uncertainty is considered as one of the most important problems in plant-wide optimization. In this article, first, a stochastic programming model with uniform distribution assumption is developed for refinery production planning under demand uncertainty, and then a hybrid programming model incorporating the linear programming model with the stochastic programming one by a weight factor is proposed. Subsequently, piecewise linear approximation functions are derived and applied to solve the hybrid programming model-under uniform distribution assumption. Case studies show that the linear approximation algorithm is effective to solve.the hybrid programming model, along with an error≤0.5% when the deviatiorgmean≤20%. The simulation results indicate that the hybrid programming model with an appropriate weight factor （0.1-0.2） can effectively improve the optimal operational strategies under demand uncertainty, achieving higher profit than the linear programming model and the stochastic programming one with about 1.3% and 0.4% enhancement, respectavely.

Hybrid Navigation System Based Autonomous Positioning and Path Planning for Mobile Robots

Positioning and navigation technology is a new trend of research in mobile robot area.Existing researches focus on the indoor industrial problems,while many application fields are in the outdoor environment,which put forward higher requirements for sensor selection and navigation scheme.In this paper,a complete hybrid navigation system for a class of mobile robots with load tasks and docking tasks is presented.The work can realize large-range autonomous positioning and path planning for mobile robots in unstructured scenarios.The autonomous positioning is achieved by adopting suitable guidance methods to meet different application requirements and accuracy requirements in conditions of different distances.Based on the Bezier curve,a path planning scheme is proposed and a motion controller is designed to make the mobile robot follow the target path.The Kalman filter is established to process the guidance signals and control outputs of the motion controller.Finally,the autonomous positioning and docking experiment are carried out.The results of the research verify the effectiveness of the hybrid navigation,which can be used in autonomous warehousing logistics and multi-mobile robot system.

Optimal Bayesian Sampling Plans Based on Hybrid Type-Ⅱ Censored Samples

The Bayesian sampling plans for exponential distributions are studied based on type-Ⅱ hybrid censored samples. The optimal Bayesian sampling plan is derived under a general loss function which includes the sampling cost, time-consuming cost, salvage value,and decision loss. It is employed to determine the Bayes risk and the corresponding optimal sampling plan. An explicit expression of the Bayes risk is derived. Furthermore,for the conjugate prior distribution,the closed-form formula of the Bayes decision rule can be obtained under either the linear or quadratic decision loss.

Dosimetric consequences of tumor volume changes after kilovoltage cone-beam computed tomography for non-operative lung cancer during adaptive intensity-modulated radiotherapy or fractionated stereotactic radiotherapy

Objective The aim of this study was to investigate tumor volume changes with kilovoltage cone-beam computed tomography （kV-CBCT） and their dosimetric consequences for non-operative lung cancer during intensity-modulated radiotherapy （IMRT） or fractionated stereotactic radiotherapy. Methods Eighteen patients with non-operative lung cancer who received IMRT consisting of 1.8-2.2 Gy/fraction and five fractions per week or stereotactic radiotherapy with 5-8 Gy/fraction and three fractions a week were studied, kV-CBCT was performed once per week during IMRT and at every fraction during stereotactic radiotherapy. The gross tumor volume （GTV） was contoured on the kV-CBCT images, and adaptive treatment plans were created using merged kV-CBCT and primary planning computed tomogra- phy image sets. Tumor volume changes and dosimetric parameters, including the minimum dose to 95% （D95） or 1% （D1） of the planning target volume （PTV）, mean lung dose （MLD）, and volume of lung tissue that received more than 5 （Vs）, 10 （Vl0）, 20 （V20）, and 30 （V30） Gy were retrospectively analyzed. Results The average maximum change in GTV observed during IMRT or fractionated stereotactic radio- therapy was -25.85% （range, -13.09% --56.76%）. The D95 and Dr of PTV for the adaptive treatment plans in all patients were not significantly different from those for the initial or former adaptive treatment plans. In patients with tumor volume changes of 〉20% in the third or fourth week of treatment during IMRT, adap- tive treatment plans offered clinically meaningful decreases in MLD and V5, V10, V20, and V30; however, in patients with tumor volume changes of 〈 20% in the third or fourth week of treatment as well as in patients with stereotactic radiotherapy, there were no significant or clinically meaningful decreases in the dosimetric parameters. Conclusion Adaptive treatment planning for decreasing tumor volume during IMRT may be beneficial for patients who experience tumor volume changes of 〉20% in the third or fourth week of treatment.

改进Hybrid A^(*)的拖挂式移动机器人路径规划算法

为了提高传统Hybrid A^(*)算法的路径规划效率和安全系数,提出一种改进的Hybrid A^(*)路径规划算法,并将此算法应用在拖挂式移动机器人系统上。首先,对启发函数进行改进,以减少路径规划过程中的计算量,从而提高规划效率;其次,设计障碍惩罚函数,进而实现提前避开行进路径上的障碍物,避免在U型障碍中陷入局部最优解;最后,考虑到拖挂式机器人模型结构的特殊性,无法将其视为质点,为此采用碰撞检测算法来提高规划路径的合理性和准确性。仿真试验验证,提出的改进Hybrid A^(*)路径规划算法可适用于拖挂式机器人系统,且具有规划效率和安全性能高、路径平滑等特点,为其在实际应用中的路径规划提供理论依据。

基于逆向Hybrid A^(*)的自动泊车路径规划

为提高自动泊车路径规划的实时性,设计一种改进型Hybrid A^(*)路径规划算法。该算法针对空间狭窄的泊车场景,对路径的起始点和目标点进行互换,成功且高效地规划出安全的泊车路径。实验结果表明,所提出的逆向扩展Hybrid A^(*)算法在搜索路径时能够减少无效节点的扩展,提高路径规划的实时性。

Using genetic/simulated annealing algorithm to solve disassembly sequence planning

Disassembly sequence planning （DSP） plays a significant role in maintenance planning of the aircraft. It is used during the design stage for the analysis of maintainability of the aircraft. To solve product disassembly sequence planning problems efficiently, a product disassembly hybrid graph model, which describes the connection, non-connection and precedence relationships between the product parts, is established based on the characteristic of disassembly. Farther, the optimization model is provided to optimize disassembly sequence. And the solution methodology based on the genetic/simulated annealing algorithm with binaxy-tree algorithm is given. Finally, an example is analyzed in detail, and the result shows that the model is correct and efficient.

A Multi-Objective Hybrid Genetic Based Optimization for External Beam Radiation

A multi-objective hybrid genetic based optimization algorithm is proposed according to the multi-objective property of inverse planning. It is based on hybrid adaptive genetic algorithm which combines the simulated annealing, uses adaptive crossover and mutation, and adopts niched tournament selection. The result of the test calculation demonstrates that an excellent converging speed can be achieved using this approach.

System security of hybrid AC-HVDC-systems challenges and new approaches for combined security assessment,preventive optimization and curative actions

The intense application of Voltage Source Converter based HVDC interconnections and grids will result in a hybrid AC-HVDC-system. The operation of such a system becomes complex regarding system security and system operation. This paper describes major challenges and proposes potential solutions, including a combined security assessment, preventive optimization and curative actions. A coordination of both systems enables an efficient application of existing transport capacity.

Improved algorithms to plan missions for agile earth observation satellites

This study concentrates of the new generation of the agile （AEOS）. AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satellites. Hence, the mission planning and scheduling of AEOS is a popular research problem. This research investigates AEOS characteristics and establishes a mission planning model based on the working principle and constraints of AEOS as per analysis. To solve the scheduling issue of AEOS, several improved algorithms are developed. Simulation results suggest that these algorithms are effective.

Multi-objective Trajectory Planning Method based on the Improved Elitist Non-dominated Sorting Genetic Algorithm

Robot manipulators perform a point-point task under kinematic and dynamic constraints.Due to multi-degreeof-freedom coupling characteristics,it is difficult to find a better desired trajectory.In this paper,a multi-objective trajectory planning approach based on an improved elitist non-dominated sorting genetic algorithm(INSGA-II)is proposed.Trajectory function is planned with a new composite polynomial that by combining of quintic polynomials with cubic Bezier curves.Then,an INSGA-II,by introducing three genetic operators:ranking group selection(RGS),direction-based crossover(DBX)and adaptive precision-controllable mutation(APCM),is developed to optimize travelling time and torque fluctuation.Inverted generational distance,hypervolume and optimizer overhead are selected to evaluate the convergence,diversity and computational effort of algorithms.The optimal solution is determined via fuzzy comprehensive evaluation to obtain the optimal trajectory.Taking a serial-parallel hybrid manipulator as instance,the velocity and acceleration profiles obtained using this composite polynomial are compared with those obtained using a quintic B-spline method.The effectiveness and practicability of the proposed method are verified by simulation results.This research proposes a trajectory optimization method which can offer a better solution with efficiency and stability for a point-to-point task of robot manipulators.

Inference and optimal design on step-stress partially accelerated life test for hybrid system with masked data

Under Type-Ⅱ progressively hybrid censoring, this paper discusses statistical inference and optimal design on stepstress partially accelerated life test for hybrid system in presence of masked data. It is assumed that the lifetime of the component in hybrid systems follows independent and identical modified Weibull distributions. The maximum likelihood estimations(MLEs)of the unknown parameters, acceleration factor and reliability indexes are derived by using the Newton-Raphson algorithm. The asymptotic variance-covariance matrix and the approximate confidence intervals are obtained based on normal approximation to the asymptotic distribution of MLEs of model parameters. Moreover,two bootstrap confidence intervals are constructed by using the parametric bootstrap method. The optimal time of changing stress levels is determined under D-optimality and A-optimality criteria.Finally, the Monte Carlo simulation study is carried out to illustrate the proposed procedures.

Multi-objective coordination optimal model for new power intelligence center based on hybrid algorithm

In order to resolve the coordination and optimization of the power network planning effectively, on the basis of introducing the concept of power intelligence center (PIC), the key factor power flow, line investment and load that impact generation sector, transmission sector and dispatching center in PIC were analyzed and a multi-objective coordination optimal model for new power intelligence center (NPIC) was established. To ensure the reliability and coordination of power grid and reduce investment cost, two aspects were optimized. The evolutionary algorithm was introduced to solve optimal power flow problem and the fitness function was improved to ensure the minimum cost of power generation. The gray particle swarm optimization (GPSO) algorithm was used to forecast load accurately, which can ensure the network with high reliability. On this basis, the multi-objective coordination optimal model which was more practical and in line with the need of the electricity market was proposed, then the coordination model was effectively solved through the improved particle swarm optimization algorithm, and the corresponding algorithm was obtained. The optimization of IEEE30 node system shows that the evolutionary algorithm can effectively solve the problem of optimal power flow. The average load forecasting of GPSO is 26.97 MW, which has an error of 0.34 MW compared with the actual load. The algorithm has higher forecasting accuracy. The multi-objective coordination optimal model for NPIC can effectively process the coordination and optimization problem of power network.

The RHSA Strategy for the Allocation of Outbound Containers Based on the Hybrid Genetic Algorithm

Secure storage yard is one of the optimal core goals of container transportation;thus,making the necessary storage arrangements has become the most crucial part of the container terminal management systems（CTMS）.This paper investigates a random hybrid stacking algorithm（RHSA） for outbound containers that randomly enter the yard.In the first stage of RHSA,the distribution among blocks was analyzed with respect to the utilization ratio.In the second stage,the optimization of bay configuration was carried out by using the hybrid genetic algorithm.Moreover,an experiment was performed to test the RHSA.The results show that the explored algorithm is useful to increase the efficiency.

Collision avoidance for a mobile robot based on radial basis function hybrid force control technique

Collision avoidance is always difficult in the planning path for a mobile robot. In this paper, the virtual force field between a mobile robot and an obstacle is formed and regulated to maintain a desired distance by hybrid force control algorithm. Since uncertainties from robot dynamics and obstacle degrade the performance of a collision avoidance task, intelligent control is used to compensate for the uncertainties. A radial basis function （RBF） neural network is used to regulate the force field of an accurate distance between a robot and an obstacle in this paper and then simulation studies are conducted to confirm that the proposed algorithm is effective.

Adaptive Region Boosting method with biased entropy for path planning in changing environment

Path planning in changing environments with difficult regions, such as narrow passages and obstacle boundaries, creates significant chal- lenges. As the obstacles in W-space move frequently, the crowd degree of C-space changes accordingly. Therefore, in order to dynamically improve the sampling quality, it is appreciated for a planner to rapidly approximate the crowd degree of different parts of the C-space, and boost sample densities with them based on their difficulty levels. In this paper, a novel approach called Adaptive Region Boosting （ARB） is proposed to increase the sampling density for difficult areas with different strategies. What＇s more, a new criterion, called biased entropy, is proposed to evaluate the difficult degree of a region. The new criterion takes into account both temporal and spatial information of a specific C-space region, in order to make a thorough assessment to a local area. Three groups of experiments are conducted based on a dual-manipulator system with 12 DoFs. Experimental results indicate that ARB effectively improves the success rate and outperforms all the other related methods in various dynamical scenarios.

Comparison between 4D robust optimization methods for carbon-ion treatment planning

Intensity-modulated particle therapy(IMPT)with carbon ions is comparatively susceptible to various uncertainties caused by breathing motion,including range,setup,and target positioning uncertainties.To determine relative biological effectiveness-weighted dose(RWD)distributions that are resilient to these uncertainties,the reference phase-based four-dimensional(4D)robust optimization(RP-4DRO)and each phase-based 4D robust optimization(EP-4DRO)method in carbon-ion IMPT treatment planning were evaluated and compared.Based on RWD distributions,4DRO methods were compared with 4D conventional optimization using planning target volume(PTV)margins(PTV-based optimization)to assess the effectiveness of the robust optimization methods.Carbon-ion IMPT treatment planning was conducted in a cohort of five lung cancer patients.The results indicated that the EP-4DRO method provided better robustness(P=0.080)and improved plan quality(P=0.225)for the clinical target volume(CTV)in the individual respiratory phase when compared with the PTV-based optimization.Compared with the PTV-based optimization,the RP-4DRO method ensured the robustness(P=0.022)of the dose distributions in the reference breathing phase,albeit with a slight sacrifice of the target coverage(P=0.450).Both 4DRO methods successfully maintained the doses delivered to the organs at risk(OARs)below tolerable levels,which were lower than the doses in the PTV-based optimization(P<0.05).Furthermore,the RP-4DRO method exhibited significantly superior performance when compared with the EP-4DRO method in enhancing overall OAR sparing in either the individual respiratory phase or reference respiratory phase(P<0.05).In general,both 4DRO methods outperformed the PTV-based optimization in terms of OAR sparing and robustness.