A Novel Prescribed-Performance Path-Following Problem for Non-Holonomic Vehicles

The issue of achieving prescribed-performance path following in robotics is addressed in this paper,where the aim is to ensure that a desired path within a specified region is accu-rately converged to by the controlled vehicle.In this context,a novel form of the prescribed performance guiding vector field is introduced,accompanied by a prescribed-time sliding mode con-trol approach.Furthermore,the interdependence among the pre-scribed parameters is discussed.To validate the effectiveness of the proposed method,numerical simulations are presented to demonstrate the efficacy of the approach.

Guaranteed Cost Attitude Tracking Control for Uncertain Quadrotor Unmanned Aerial Vehicle Under Safety Constraints

In this paper,guaranteed cost attitude tracking con-trol for uncertain quadrotor unmanned aerial vehicle(QUAV)under safety constraints is studied.First,an augmented system is constructed by the tracking error system and reference system.This transformation aims to convert the tracking control prob-lem into a stabilization control problem.Then,control barrier function and disturbance attenuation function are designed to characterize the violations of safety constraints and tolerance of uncertain disturbances,and they are incorporated into the reward function as penalty items.Based on the modified reward function,the problem is simplified as the optimal regulation problem of the nominal augmented system,and a new Hamilton-Jacobi-Bellman equation is developed.Finally,critic-only rein-forcement learning algorithm with a concurrent learning tech-nique is employed to solve the Hamilton-Jacobi-Bellman equa-tion and obtain the optimal controller.The proposed algorithm can not only ensure the reward function within an upper bound in the presence of uncertain disturbances,but also enforce safety constraints.The performance of the algorithm is evaluated by the numerical simulation.

Lie-form invariance of non-holonomic systems with unilateral constraints

In this paper the Lie-form invariance of the non-holonomic systems with unilateral constraints is studied. The definition and the criterion of the Lie-form invariance of the system are given. The generalized Hojman conserved quantity and a new type of conserved quantity deduced from the Lie-form invariance are obtained. Finally, an example is presented to illustrate the application of the results.

Dynamics and Stability of Blind Grasping of a 3-Dimensional Object under Non-holonomic Constraints

A mathematical model expressing the motion of a pair of multi-DOF robot fingers with hemi-spherical ends, grasping a 3-D rigid object with parallel fiat surfaces, is derived, together with non-holonomic constraints. By referring to the fact that humans grasp an object in the form of precision prehension, dynamically and stably by opposable forces, between the thumb and another finger （index or middle finger）, a simple control signal constructed from finger-thumb opposition is proposed, and shown to realize stable grasping in a dynamic sense without using object information or external sensing （this is called ＂blind grasp＂ in this paper）. The stability of grasping with force/torque balance under non-holonomic constraints is analyzed on the basis of a new concept named ＂stability on a manifold＂. Preliminary simulation results are shown to verify the validity of the theoretical results.

THE FUNDAMENTAL EQUATIONS OF DYNAMICS USINGREPRESENTATION OF QUASI-COORDINATES IN THE SPACEOF NON-LINEAR NON-HOLONOMIC CONSTRAINTS

The dot product of the bases vectors on the super-surface of the non-linear nonholonomic constraints with one order, expressed by quasi-coorfinates, and Mishirskiiequalions are regarded as the fundamental equations of dynamics with non-linear andnon-holononlic constraints in one order for the system of the variable mass. From thesethe variant ddferential-equations of dynamics expressed by quasi-coordinates arederived. The fundamental equations of dynamics are compatible with the principle ofJourdain. A case is cited.

A Hybrid Genetic Algorithm for Vehicle Routing Problem with Complex Constraints

Most research on the Vehicle Routing Problem （VRP） is focused on standard conditions, which is not suitable for specific cases. A Hybrid Genetic Algorithm is proposed to solve a Vehicle Routing Problem （VRP） with complex side constraints. A novel coding method is designed especially for side constraints. A greedy algorithm combined with a random algorithm is introduced to enable the diversity of the initial population, as well as a local optimization algorithm employed to improve the searching efficiency. In order to evaluate the performance, this mechanism has been implemented in an oil distribution center, the experimental and executing results show that the near global optimal solution can be easily and quickly obtained by this method, and the solution is definitely satisfactory in the VRP application.

The Information Modeling and Intelligent Optimization Method for Logistics Vehicle Routing and Scheduling with Multi-objective and Multi-constraint

The vehicle routing and scheduling (VRS) problem with multi-objective and multi-constraint is analyzed, considering the complexity of the modern logistics in city economy and daily life based on the system engineering. The objective and constraint includes loading, the dispatch and arrival time, transportation conditions,total cost,etc. An information model and a mathematical model are built,and a method based on knowledge and biologic immunity is put forward for optimizing and evaluating the programs dimensions in vehicle routing and scheduling with multi-objective and multi-constraints. The proposed model and method are illustrated in a case study concerning a transport network, and the result shows that more optimization solutions can be easily obtained and the method is efficient and feasible. Comparing with the standard GA and the standard GA without time constraint,the computational time of the algorithm is less in this paper. And the probability of gaining optimal solution is bigger and the result is better under the condition of multi-constraint.

Comparison of Interval Constraint Propagation Algorithms for Vehicle Localization

Interval constraint propagation (ICP) algorithms allow to solve problems described as constraint satisfaction problems (CSP). ICP has been successfully applied to vehicle localization in the last few years. Once the localization problem has been stated, a large class of ICP solvers can be used. This paper compares a few ICP algorithms, using the same experimental data, in order to rank their performances in terms of accuracy and computing time.

Terminal Angular Constraint Integrated Guidance and Control for Flexible Hypersonic Vehicle with Dead-Zone Input Nonlinearity

This paper presents an integrated guidance and control model for a flexible hypersonic vehicle with terminal angular constraints.The integrated guidance and control model is bounded and the dead-zone input nonlinearity is considered in the system dynamics.The line of sight angle,line of sight angle rate,attack angle and pitch rate are involved in the integrated guidance and control system.The controller is designed with a backstepping method,in which a first order filter is employed to avoid the differential explosion.The full tuned radial basis function(RBF)neural network(NN)is used to approximate the system dynamics with robust item coping with the reconstruction errors,the exactitude model requirement is reduced in the controller design.In the last step of backstepping method design,the adaptive control with Nussbaum function is used for the unknown dynamics with a time-varying control gain function.The uniform ultimate boundedness stability of the control system is proved.The simulation results validate the effectiveness of the controller design.

A leveling mechanism for the platform based on booms-constraint control of aerial vehicle

In order to achieve an automatic leveling function for work platforms of aerial vehicles with mixed-booms( MAV) in full elevating domain,an auto-leveling mechanism for the platform is proposed based on a control method of booms-constraint,where mixed-boom structures and elevating characteristics are considered. Three models of constraint strategies include non-constraint model,elevating constraint model and lowering constraint model,which is designed to meet the leveling requirements in full working extent. Through the hydro-mechatronic unified modeling,a virtual prototype model is set up based on the auto-leveling mechanism,and leveling performances of the platform are studied during booms elevating to the maximum working height and extent. Simulation results show that the control method of booms-constraint can realize auto-leveling of the platform under two typical working conditions,meanwhile a leveling deviation appears at the constrained point,but the platform inclination is adjusted in the permissible range. The control method does not only restrict booms' freedom elevating to a certain extent,but also impacts the booms extending to the maximum working range. Experimental results verify that the auto-leveling mechanism based on booms-constraint control is valid and rational,which provides an effective technology approach for development of the platform leveling of MAV.

Coalition Formation for Multiple UAVs Cooperative Search and Attack with Communication Constraints in Unknown Environment

A coalition formation algorithm is presented with limited communication ranges and delays in unknown environment,for the performance of multiple heterogeneous unmanned aerial vehicles(UAVs)in cooperative search and attack missions.The mathematic model of coalition formation is built on basis of the minimum attacking time and the minimum coalition size with satisfying resources and simultaneous strikes requirements.A communication protocol based on maximum number of hops is developed to determine the potential coalition members in dynamic network.A multistage sub-optimal coalition formation algorithm(MSOCFA)with polynomial time is established.The performances of MSOCFA and particle swarm optimization(PSO)algorithms are compared in terms of complexity,mission performance and computational time.A complex scenario is deployed to illustrate how the coalitions are formed and validate the feasibility of the MSOCFA.The effect of communication constraints(hop delay and max-hops)on mission performance is studied.The results show that it is beneficial to determine potential coalition members in a wide and deep range over the network in the presence of less delay.However,when the delays are significant,it is more advantageous to determine coalitions from among the immediate neighbors.

Experimental Tests of Autonomous Ground Vehicles with Preview

This paper describes the design and experimental tests of a path planning and reference tracking algorithm for autonomous ground vehicles. The ground vehicles under consideration are equipped with forward looking sensors that provide a preview capability over a certain horizon. A two-level control framework is proposed for real-time implementation of the model predictive control （MPC） algorithm, where the high-level performs on-line optimization to generate the best possible local reference respect to various constraints and the low-level commands the vehicle to follow realistic trajectories generated by the high-level controller. The proposed control scheme is implemented on an indoor testbed through networks with satisfactory performance.

Unified symmetry of non-holonomic singular systems

In this paper, the unified symmetry of non-holonomic singular systems is studied. The differential equations of motion of the systems are given. The definition and the criterion of the unified symmetry for the systems are presented. The Noether conserved quantity, the Hojman conserved quantity and the Mei conserved quantity are obtained. An example is given to illustrate the application of the results.

Spatiotemporal distance embedded hybrid ant colony algorithm for a kind of vehicle routing problem with constraints

We investigate a kind of vehicle routing problem with constraints(VRPC)in the car-sharing mobility environment,where the problem is based on user orders,and each order has a reservation time limit and two location point transitions,origin and destination.It is a typical extended vehicle routing problem(VRP)with both time and space constraints.We consider the VRPC problem characteristics and establish a vehicle scheduling model to minimize operating costs and maximize user(or passenger)experience.To solve the scheduling model more accurately,a spatiotemporal distance representation function is defined based on the temporal and spatial properties of the customer,and a spatiotemporal distance embedded hybrid ant colony algorithm(HACA-ST)is proposed.The algorithm can be divided into two stages.First,through spatiotemporal clustering,the spatiotemporal distance between users is the main measure used to classify customers in categories,which helps provide heuristic information for problem solving.Second,an improved ant colony algorithm(ACO)is proposed to optimize the solution by combining a labor division strategy and the spatiotemporal distance function to obtain the final scheduling route.Computational analysis is carried out based on existing data sets and simulated urban instances.Compared with other heuristic algorithms,HACA-ST reduces the length of the shortest route by 2%–14%in benchmark instances.In VRPC testing instances,concerning the combined cost,HACA-ST has competitive cost compared to existing VRP-related algorithms.Finally,we provide two actual urban scenarios to further verify the effectiveness of the proposed algorithm.

面向复杂物流配送场景的车辆路径规划多任务辅助进化算法

在现代社会中,复杂物流配送场景的车辆路径规划问题(Vehicle routing problem,VRP)一般带有时间窗约束且需要提供同时取送货的服务.这种复杂物流配送场景的车辆路径规划问题是NP-难问题.当其规模逐渐增大时,一般的数学规划方法难以求解,通常使用启发式方法在限定时间内求得较优解.然而,传统的启发式方法从原大规模问题直接开始搜索,无法利用先前相关的优化知识,导致收敛速度较慢.因此,提出面向复杂物流配送场景的车辆路径规划多任务辅助进化算法(Multitask-based assisted evolutionary algorithm,MBEA),通过使用迁移优化方法加快算法收敛速度,其主要思想是通过构造多个简单且相似的子任务用于辅助优化原大规模问题.首先从原大规模问题中随机选择一部分客户订单用于构建多个不同的相似优化子任务,然后使用进化多任务(Evolutional multitasking,EMT)方法用于生成原大规模问题和优化子任务的候选解.由于优化子任务相对简单且与原大规模问题相似,其搜索得到的路径特征可以通过任务之间的知识迁移辅助优化原大规模问题,从而加快其求解速度.最后,提出的算法在京东物流公司快递取送货数据集上进行验证,其路径规划效果优于当前最新提出的路径规划算法.

基于紧约束鲁棒模型预测控制的无人车辆轨迹跟踪控制

针对无人驾驶车辆的轨迹跟踪问题,基于车辆非线性动力学模型,设计了一种紧约束鲁棒模型预测控制(MPC)方法。首先,利用魔术公式轮胎模型,结合车辆3自由度系统,构建了具有有界干扰的车辆线性离散误差模型;其次,采用紧约束控制策略设计系统的鲁棒优化问题;最后,通过在线优化得到最优控制序列,并离线构造之后多个时刻满足约束条件的可行控制序列。仿真结果表明,所提算法能够使车辆稳定快速跟踪上参考轨迹并有效提高系统计算资源利用率。

面向可打击区域的高超声速飞行器制导控制一体化方法研究

针对高超声速飞行器制导和控制系统分离设计方法的不足,提出通过制导控制一体化设计提升其可打击区域的方法。首先,在建立高超声速制导控制一体化模型的基础上,揭示了其通过控制弹体坐标系与地面坐标系的牵连加速度来跟踪制导指令的工作原理。其次,通过典型弹道的数值仿真,对比分析了制导控制一体化设计相较于分离设计的控制信号差异。最后,通过确定制导控制分离设计及一体化设计的高超声速飞行器可打击区域的方式,从大样本仿真角度体现制导控制一体化的优势。结果表明,制导控制一体化设计可以放宽对可用过载的需求且有助于提升高超声飞行器的可打击区域。

考虑个体客户满意度的多车程生鲜品配送路径优化

随着生鲜电商的兴起,生鲜品的道路运输量越来越大,其配送任务越来越复杂,提升客户满意度是生鲜品配送的重要目标组成。文章以一个具有配送自主权的生鲜品供应商为研究对象,以整个计划周期内生鲜品配送的车辆运输成本最小及客户满意度最大为双目标,且客户满意度从顾客的个体视角考虑,构建双目标多车程带时间窗及考虑订单打包时间的车辆路径优化模型。基于ε-约束方法,设计了两阶段变邻域搜索(VNS)和模拟退火(SA)相结合的元启发式算法,基于车辆构造初始路线,并针对性地提出了3点改进措施,包括通过数学性质推导下界、减少求解过程中不必要的搜索空间以及规避求解过程中被支配解的后优化处理。最后,以Solomon标准算例为基础构造算例对所设计的算法进行了计算实验,其求解结果表明:其设计的算法具有有效性,并通过敏感性分析得出相关管理启示。

基于改进哈里斯鹰算法的无人飞行器路径规划

针对无人飞行器三维路径规划问题,提出一种基于哈里斯鹰优化(Harris hawks optimization,HHO)算法的无人飞行器三维路径规划算法。首先根据路径规划代价指标和无人飞行器自身性能,建立路径规划模型确立代价函数和约束条件。接着针对传统HHO算法的不足,引入非线性能量因子来平衡全局搜索和局部搜索的关系,使算法避免陷入局部最小值;引入混沌映射对HHO算法进行初始化种群并对其进行局部混沌搜索,增强算法种群多样性和搜索能力。最后通过仿真实验证明,改进的哈里斯鹰优化(improvement Harris hawks optimization,IHHO)算法可以有效规划出安全的无人飞行器航线,并且能够跳出局部最小值和具备较优的收敛速度。