Optimal search path planning of UUV in battlefeld ambush scene

Aiming at the practical application of Unmanned Underwater Vehicle(UUV)in underwater combat,this paper proposes a battlefield ambush scene with UUV considering ocean current.Firstly,by establishing these mathematical models of ocean current environment,target movement,and sonar detection,the probability calculation methods of single UUV searching target and multiple UUV cooperatively searching target are given respectively.Then,based on the Hybrid Quantum-behaved Particle Swarm Optimization(HQPSO)algorithm,the path with the highest target search probability is found.Finally,through simulation calculations,the influence of different UUV parameters and target parameters on the target search probability is analyzed,and the minimum number of UUVs that need to be deployed to complete the ambush task is demonstrated,and the optimal search path scheme is obtained.The method proposed in this paper provides a theoretical basis for the practical application of UUV in the future combat.

Bi-level Optimal Planning of Voltage Regulator in Distribution Systems Considering Maximization of Incentive-based Photovoltaic Energy Integration

This paper focuses on optimal voltage regulator(VR)planning to maximize the photovoltaic(PV)energy integration in distribution grids.To describe the amount of dynamic PV energy that can be integrated into the power system,the concept of PV accommodation capability(PVAC)is introduced and modeled with optimization.Our proposed planning model is formulated as a Benders decomposition based bi-level stochastic optimization problem.In the upper-level problem,VR planning decisions and PVAC are determined via mixed integer linear programming(MILP)before considering uncertainty.Then in the lower-level problem,the feasibility of first-level results is checked by critical network constraints(e.g.voltage magnitude constraints and line capacity constraints)under uncertainties considered by time-varying loads and PV generations.In this paper,these uncertainties are represented in the form of operational scenarios,which are generated by the Gaussian copula theory and reduced by a well-studied backward-reduction algorithm.The modified IEEE 33-node distribution grid is utilized to verify the effectiveness of the proposed model.The results demonstrate that a PV energy integration can be significantly enhanced after optimal voltage regulator planning.

Efficient Route Planning for Real-Time Demand-Responsive Transit

Demand Responsive Transit (DRT) responds to the dynamic users’ requests without any fixed routes and timetablesand determines the stop and the start according to the demands. This study explores the optimization of dynamicvehicle scheduling and real-time route planning in urban public transportation systems, with a focus on busservices. It addresses the limitations of current shared mobility routing algorithms, which are primarily designedfor simpler, single origin/destination scenarios, and do not meet the complex demands of bus transit systems. Theresearch introduces an route planning algorithm designed to dynamically accommodate passenger travel needsand enable real-time route modifications. Unlike traditional methods, this algorithm leverages a queue-based,multi-objective heuristic A∗ approach, offering a solution to the inflexibility and limited coverage of suburbanbus routes. Also, this study conducts a comparative analysis of the proposed algorithm with solutions based onGenetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO), focusing on calculation time, routelength, passenger waiting time, boarding time, and detour rate. The findings demonstrate that the proposedalgorithmsignificantly enhances route planning speed, achieving an 80–100-fold increase in efficiency over existingmodels, thereby supporting the real-time demands of Demand-Responsive Transportation (DRT) systems. Thestudy concludes that this algorithm not only optimizes route planning in bus transit but also presents a scalablesolution for improving urban mobility.

Research on Evacuation Path Planning Based on Improved Sparrow Search Algorithm

Reducing casualties and property losses through effective evacuation route planning has been a key focus for researchers in recent years.As part of this effort,an enhanced sparrow search algorithm(MSSA)was proposed.Firstly,the Golden Sine algorithm and a nonlinear weight factor optimization strategy were added in the discoverer position update stage of the SSA algorithm.Secondly,the Cauchy-Gaussian perturbation was applied to the optimal position of the SSA algorithm to improve its ability to jump out of local optima.Finally,the local search mechanism based on the mountain climbing method was incorporated into the local search stage of the SSA algorithm,improving its local search ability.To evaluate the effectiveness of the proposed algorithm,the Whale Algorithm,Gray Wolf Algorithm,Improved Gray Wolf Algorithm,Sparrow Search Algorithm,and MSSA Algorithm were employed to solve various test functions.The accuracy and convergence speed of each algorithm were then compared and analyzed.The results indicate that the MSSA algorithm has superior solving ability and stability compared to other algorithms.To further validate the enhanced algorithm’s capabilities for path planning,evacuation experiments were conducted using different maps featuring various obstacle types.Additionally,a multi-exit evacuation scenario was constructed according to the actual building environment of a teaching building.Both the sparrow search algorithm and MSSA algorithm were employed in the simulation experiment for multiexit evacuation path planning.The findings demonstrate that the MSSA algorithm outperforms the comparison algorithm,showcasing its greater advantages and higher application potential.

Study on site selection planning of urban electric vehicle charging station

The large-scale development of electric vehicles(EVs)requires numerous charging stations to serve them,and the charging stations should be reasonably laid out and planned according to the charging demand of electric vehicles.Considering the costs of both operators and users,a site selection model for optimal layout planning of charging stations is constructed,and a queuing theory approach is used to determine the charging pile configuration to meet the charging demand in the planning area.To solve the difficulties of particle swarm global optimization search,the improved random drift particle swarm optimization(IRDPSO)and Voronoi diagram are used to jointly solve for the optimal layout of electric vehicles.The final arithmetic analysis verifies the feasibility and practicality of the model and algorithm,and the results show that the total social cost is minimized when the charging station is 9,the location of the charging station is close to the center of gravity and the layout is reasonable.

Path Planning for Robotic Arms Based on an Improved RRT Algorithm

The burgeoning robotics industry has catalyzed significant strides in the development and deployment of industrial and service robotic arms, positioning path planning as a pivotal facet for augmenting their operational safety and efficiency. Existing path planning algorithms, while capable of delineating feasible trajectories, often fall short of achieving optimality, particularly concerning path length, search duration, and success likelihood. This study introduces an enhanced Rapidly-Exploring Random Tree (RRT) algorithm, meticulously designed to rectify the issues of node redundancy and the compromised path quality endemic to conventional RRT approaches. Through the integration of an adaptive pruning mechanism and a dynamic elliptical search strategy within the Informed RRT* framework, our algorithm efficiently refines the search tree by discarding branches that surpass the cost of the optimal path, thereby refining the search space and significantly boosting efficiency. Extensive comparative analysis across both two-dimensional and three-dimensional simulation settings underscores the algorithm’s proficiency in markedly improving path precision and search velocity, signifying a breakthrough in the domain of robotic arm path planning.

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.

Research on Grid Planning of Dual Power Distribution Network Based on Parallel Ant Colony Optimization Algorithm

A distribution network plays an extremely important role in the safe and efficient operation of a power grid.As the core part of a power grid’s operation,a distribution network will have a significant impact on the safety and reliability of residential electricity consumption.it is necessary to actively plan and modify the distribution network’s structure in the power grid,improve the quality of the distribution network,and optimize the planning of the distribution network,so that the network can be fully utilized to meet the needs of electricity consumption.In this paper,a distribution network grid planning algorithm based on the reliability of electricity consumption was completed using ant colony algorithm.For the distribution network structure planning of dual power sources,the parallel ant colony algorithm was used to prove that the premise of parallelism is the interactive process of ant colonies,and the dual power distribution network structure model is established based on the principle of the lowest cost.The artificial ants in the algorithm were compared with real ants in nature,and the basic steps and working principle of the ant colony optimization algorithm was studied with the help of the travelling salesman problem(TSP).Then,the limitations of the ant colony algorithm were analyzed,and an improvement strategy was proposed by using python for digital simulation.The results demonstrated the reliability of model-building and algorithm improvement.

Nonlinear optimal model and solving algorithms for platform planning problem in battlefield

Platform planning is one of the important problems in the command and control(C2) field. Hereto, we analyze the platform planning problem and present nonlinear optimal model aiming at maximizing the task completion qualities. Firstly, we take into account the relation among tasks and build the single task nonlinear optimal model with a set of platform constraints. The Lagrange relaxation method and the pruning strategy are used to solve the model. Secondly, this paper presents optimization-based planning algorithms for efficiently allocating platforms to multiple tasks. To achieve the balance of the resource assignments among tasks, the m-best assignment algorithm and the pair-wise exchange(PWE)method are used to maximize multiple tasks completion qualities.Finally, a series of experiments are designed to verify the superiority and effectiveness of the proposed model and algorithms.

Optimal Motion Planning for Differentially Flat Underactuated Mechanical Systems

Underactuated mechanical system has less independent inputs than the degrees of freedom（DOF） of the mechanism. The energy efficiency of this class of mechanical systems is an essential problem in practice. On the basis of the sufficient and necessary condition that concludes a single input nonlinear system is differentially flat, it is shown that the flat output of the single input underactuated mechanical system can be obtained by finding a smooth output function such that the relative degree of the system equals to the dimension of the state space. If the flat output of the underactuated system can be solved explicitly, and by constructing a smooth curve with satisfying given boundary conditions in fiat output space, an energy efficiency optimization method is proposed for the motion planning of the differentially flat underactuated mechanical systems. The inertia wheel pendulum is used to verify the proposed optimization method, and some numerical simulations show that the presented optimal motion planning method can efficaciously reduce the energy cost for given control tasks.

Energy-Optimal Braking Control Using a Double-Layer Scheme for Trajectory Planning and Tracking of Connected Electric Vehicles

Most researches focus on the regenerative braking system design in vehicle components control and braking torque distribution,few combine the connected vehicle technologies into braking velocity planning.If the braking intention is accessed by the vehicle-to-everything communication,the electric vehicles(EVs)could plan the braking velocity for recovering more vehicle kinetic energy.Therefore,this paper presents an energy-optimal braking strategy(EOBS)to improve the energy efficiency of EVs with the consideration of shared braking intention.First,a double-layer control scheme is formulated.In the upper-layer,an energy-optimal braking problem with accessed braking intention is formulated and solved by the distance-based dynamic programming algorithm,which could derive the energy-optimal braking trajectory.In the lower-layer,the nonlinear time-varying vehicle longitudinal dynamics is transformed to the linear time-varying system,then an efficient model predictive controller is designed and solved by quadratic programming algorithm to track the original energy-optimal braking trajectory while ensuring braking comfort and safety.Several simulations are conducted by jointing MATLAB and CarSim,the results demonstrated the proposed EOBS achieves prominent regeneration energy improvement than the regular constant deceleration braking strategy.Finally,the energy-optimal braking mechanism of EVs is investigated based on the analysis of braking deceleration,battery charging power,and motor efficiency,which could be a guide to real-time control.

Trajectory Planning and Optimal Lateral Stability Control under Multiple Barriers for Intelligent Vehicle

Based on analysis and evaluation on the circular, cosine type, constant-speed offset type and ladder type lane change trajectory, this paper proposes an intelligent vehicle lane change trajectory model under multiple barriers, proposes its dynamic constraints in the light of the cellular automata theory, obtains the desired lane change trajectory using this method, and finally changes into a simple coefficient selection problem. Secondly, based on the quadratic optimal control theory, this paper proposes a state space analysis method of intelligent vehicle lateral control, and designs an optimal controller for lateral stability of H2 vehicles. The computer simulation results show that compared with other vehicle trajectory methods, the method in this paper is able to simply and rapidly describe the trajectory, and can describe the intelligent vehicle lane change trajectory under a variety of situations, wherein the controller is reliable and capable of fast convergence.

Optimal control based coordinated taxiing path planning and tracking for multiple carrier aircraft on flight deck

Coordinated taxiing planning for multiple aircraft on flight deck is of vital importance which can dramatically improve the dispatching efficiency.In this paper,first,the coordinated taxiing path planning problem is transformed into a centralized optimal control problem where collision-free conditions and mechanical limits are considered.Since the formulated optimal control problem is of large state space and highly nonlinear,an efficient hierarchical initialization technique based on the Dubins-curve method is proposed.Then,a model predictive controller is designed to track the obtained reference trajectory in the presence of initial state error and external disturbances.Numerical experiments demonstrate that the proposed“offline planningþonline tracking”framework can achieve efficient and robust coordinated taxiing planning and tracking even in the presence of initial state error and continuous external disturbances.

Application of Optimal-Jerk Trajectory Planning in Gait-balance Training Robot

To accommodate the gait and balance disorder of the elderly with age progression and the occurrence of various senile diseases,this paper proposes a novel gait balance training robot(G-Balance)based on a six degree-of-freedom parallel platform.Using the platform movement and IMU wearable sensors,two training modes,i.e.,active and passive,are developed to achieve vestibular stimulation.Virtual reality technology is applied to achieve visual stimulation.In the active training mode,the elderly actively exercises to control the posture change of the platform and the switching of the virtual scene.In the passive training mode,the platform movement is combined with the virtual scene to simulate bumpy environments,such as earthquakes,to enhance the human anti-interference ability.To achieve a smooth switching of the scene,continuous speed and acceleration of the platform motion are required in some scenarios,in which a trajectory planning algorithm is applied.This paper describes the application of the trajectory planning algorithm in the balance training mode and the optimization of jerk(differential of acceleration)based on cubic spline planning,which can reduce impact on the joint and enhance stability.

Ant Colony System Algorithm for Real-Time Globally Optimal Path Planning of Mobile Robots

为活动机器人计划的即时全球性最佳的路径的一个新奇方法基于蚂蚁殖民地系统(交流) 被建议算法。这个方法包括三步：第一步正在利用 MAKLINK 图理论建立活动机器人的空间模型，第二步正在利用 Dijkstra 算法发现一条非最优的没有碰撞的路径，并且第三步正在利用 ACS 算法优化非最优的路径的地点以便产生全球性最佳的路径。建议方法是有效的并且能在即时路径被使用活动机器人计划的计算机模拟实验表演的结果。建议方法比与优秀人材模型一起基于基因算法计划方法的路径处于集中速度，答案变化，动态集中行为，和计算效率有更好的性能，这被验证了。

Optimal control of nonholonomic motion planning for a free-falling cat

The nonholonomic motion planning of a free-falling cat is investigated. Nonholonomicity arises in a free-falling cat subject to nonintegrable angle velocity constraints or nonintegrable conservation laws. When the total angular momentum is zero, the motion equation of a free-falling cat is established based on the model of two symmetric rigid bodies and conservation of angular momentum. The control of system can be converted to the problem of nonholonomic motion planning for a free-falling cat. Based on Ritz approximation theory, the Gauss-Newton method for motion planning by a falling cat is proposed. The effectiveness of the numerical algorithm is demonstrated through simulation on model of a free-falling cat.

BASED ON WAVELET ANALYSIS TO OPTIMAL CONTROL OF MOTION PLANNING OF SPACE MANIPULATOR

The optimal control problem of nonholonomic motion planning of space manipulator was discussed. Utilizing the method of wavelet analysis, the discrete orthogonal wavelets were introduced to solve the optimal control problem, the classical Fourier basic functions were replaced by the wavelet expansion approximation. A numerical algorithm of optimal control was proposed based an wavelet analysis. The numerical simulation shows, the method is effective for nonholonomic motion planning of space manipulator.

A Fast Approach for Time Optimal and Smooth Trajectory Planning of Robot Manipulators

In this paper,a fast approach to generate time optimal and smooth trajectory has been developed and tested.Minimum time is critical for the productivity in industrial applications.Meanwhile,smooth trajectories based on cubic splines are desirable for their ability to limit vibrations and ensure the continuity of position,velocity and acceleration during the robot movement.The main feature of the approach is a satisfactory solution that can be obtained by a local modification process among each interval between two consecutive via-points.An analytical formulation simplifies the approach to smooth trajectory and few iterations are enough to determine the correct values.The approach can be applied in many robot manipulators which require high performance on time and smooth.The simulation and application of the approach on a palletizer robot are performed,and the experimental results provide evidence that the approach can realize the robot manipulators more efficiency and high smooth performance.

Optimal Operation of Multipurpose Reservoir for Irrigation Planning with Conjunctive Use of Surface and Groundwater

In the present study, a Linear Programming (LP) model is developed for the conjunctive use of surface water and ground water to obtain the optimal operating policy for a multipurpose single reservoir. The objective of the present study is to maximize the net benefit from the command area under consideration. The constraints imposed on the objective function are maximum and minimum irrigation demands, reservoir storages and canal capacity. The model takes into account the continuity constraint which includes inflows in to the reservoir, releases for irrigation, releases for hydro-power generation, evaporation losses, feeder canal releases, initial and final storages in the reservoir in each time period. The developed model is applied to the case study of Jayakwadi reservoir stage-I, built across river Godavari, Maharashtra, India. Initially the model is solved for the availability of surface water which results in net benefit of 3373.45 million rupees with irrigation intensity is 57.07%. Next the model solved by considering the availability of surface water and available potential of groundwater in the area, which results in net benefits of 3590.02 million rupees with an intensity of irrigation 58.48%. The present model takes in to account the socio-economic requirement of growing the essential crops to meet the requirement of the society. The model has also generated the canal wise optimal releases for irrigation and power, monthly utilization of groundwater, storages in the reservoir at the end of every month and corresponding head over the turbine.

The Optimal Inventory Policy for Reusable Items with Random Planning Horizon Considering Present Value

We discuss five areas of inventory model, including reusable raw material, EPQ model, optimization, random planning horizon and present value. In the traditional EPQ model, the stock-holding cost of raw material was not counted as a part of relevant cost. We explored the possibility of reducing a company’s impact on the environment and increasing their competitiveness by recycling their repair and waste disposal. The products are manufactured with reusable raw material. Our analysis takes into account the time value, and the present value method is applied to determine the optimal inventory policies for reusable items with random planning horizon. Results show how the heuristic approach can achieve global optimum. Numerical examples are given to validate the proposed system.