A geometric optimization method for the trajectory planning of flexible manipulators

Multibody System Dynamics - Lightweight and flexible robots offer an interesting answer to industrial needs for safety and efficiency. The control of such systems should be able to deal properly...     

Simultaneous dynamic optimization: A trajectory planning method for nonholonomic car-like robots

Trajectory planning in robotics refers to the process of finding a motion law that enables a robot to reach its terminal configuration, with some predefined requirements considered at the same time. This study focuses on planning the time-optimal trajectories for car-like robots. We formulate a dynamic optimization problem, where the kinematic principles are accurately described through differential equations and the constraints are strictly expressed using algebraic inequalities. The formulated dynamic optimization problem is then solved by an interior-point-method-based simultaneous approach. Compared with the prevailing methods in the field of trajectory planning, our proposed method can handle various user-specified requirements and different optimization objectives in a unified manner. Simulation results indicate that our proposal efficiently deals with different kinds of physical constraints, terminal conditions and collision-avoidance requirements that are imposed on the trajectory planning mission. Moreover, we utilize a Hamiltonian-based optimality index to evaluate how close an obtained solution is to being optimal.     

Multiobjective trajectory optimization for electric trains

This paper develops a method for generating Pareto efficient trajectories that provide optimal tradeoffs between two conflicting attributes-the total energy consumed and the total time taken to complete each trajectory. Straightforward formulations of the multiobjective optimization problem in these attributes are difficult to solve because of certain nonlinearities in train models and certain constraints on train trajectories. A discrete reformulation is developed to circumvent these difficulties and produce computationally feasible algorithms. The results from the algorithms can be used to develop operating strategies for existing systems and to compare hardware alternatives in planning new systems. An illustration is included.     

A multisine approach for trajectory optimization based on information gain

This paper presents a multisine approach for trajectory optimization based on information gain, with distance and orientation sensing to known beacons. It addresses the problem of active sensing, i.e. the selection of a robot motion or sequence of motions, which make the robot arrive in its desired goal configuration (position and orientation) with maximum accuracy, given the available sensor information. The optimal trajectory is parameterized as a linear combination of sinusoidal functions. An appropriate optimality criterion is selected which takes into account various requirements (such as maximum accuracy and minimum time). Several constraints can be formulated, e.g. with respect to collision avoidance. The optimal trajectory is then determined by numerical optimization techniques. The approach is applicable to both nonholonomic and holonomic robots. Its effectiveness is illustrated here for a nonholonomic wheeled mobile robot (WMR) in an environment with and without obstacles.     

纯方位目标定位观测器轨迹的 快速优化算法

在移动传感器网络中,观测器与目标的相对位置对目标的定位性能有重要的影响.为了提高目标的定位精度,提出了一种观测器运动轨迹的优化算法.算法把目标均方位置误差作为优化对象,使用扩展卡尔曼滤波器估计目标的位置.算法以目标和观测器的方位分布关系为基础,减小了观测器最优位置的搜索范围.仿真结果表明,使用多个观测器进行目标定位,滤波收敛速度快,定位误差小.最后给出了单个和多个观测器的"最优"运动规则.     

A Genetic Algorithm-based optimization model for supporting green transportation operations

Green Logistics (GL) has emerged as a trend in the management of the distribution of goods and the collection of end-of-life products. With its focus on maximizing the economic and environmental value by means of recycling and emission control, GL contributes to the sustainable development of industry but also requires a more comprehensive transportation scheme when conducting logistics services. This study is motivated by the practice of delivering and collecting water carboys. In this paper, a Genetic Algorithm-based optimization model (GOM) is proposed for designing a green transportation scheme of economic and environmental cost efficiency in forward and reverse logistics. Two vehicle routing models with simultaneous delivery and pickup (full or partial pickup) are formulated and solved by a Genetic Algorithm. A cost generation engine is designed to perform a comprehensive cost comparison and analysis based on a set of economic and environmental cost factors, so as to examine the impact of the two models and to suggest optimal transportation schemes. The computational experiments show that the overall cost is evidently lower in the full pickup model. Notably, the impact of product cost after recycling and reusing empty carboys on total cost is more significant than the impact of transportation cost and CO₂ emission cost. In summary, the proposed GOM is capable of suggesting a guidance for the logistics service providers, who deal with green operations, to adopt a beneficial transportation scheme so as to eventually achieve a low economic and environmental cost.     

基于事件驱动的无人机吊挂系统在线自适应轨迹规划

本文针对四旋翼无人机吊挂系统空运过程中负载摆角的非线性最优调节问题, 提出了一种基于事件驱动的在线自适应轨迹规划策略. 在事件驱动的自适应评价网框架下, 利用神经网络的逼近学习能力得到吊挂负载摆动抑制的折现最优控制律. 同时结合该控制律进一步规划四旋翼无人机的飞行轨迹, 实现了对无人机位置的精确调节和吊挂负载摆动的快速抑制, 并且显著降低了无人机机载处理器的计算负担. 然后采用基于Lyapunov稳定性的分析方法, 证明了神经网络输出权值估计误差一致最终有界, 并证明了无人机位置跟踪误差和吊挂负载摆动运动的收敛. 最后, 通过飞行对比实验验证了所提出的轨迹规划策略的有效性.     

A bi-level evolutionary optimization approach for integrated production and transportation scheduling

This paper investigates an integrated production and transportation scheduling (IPTS) problem which is formulated as a bi-level mixed integer nonlinear program. This problem considers distinct realistic features widely existing in make-to-order supply chains, namely unrelated parallel-machine production environment and product batch-based delivery. An evolution-strategy-based bi-level evolutionary optimization approach is developed to handle the IPTS problem by integrating a memetic algorithm and heuristic rules. The efficiency and effectiveness of the proposed approach is evaluated by numerical experiments based on industrial data and industrial-size problems. Experimental results demonstrate that the proposed approach can effectively solve the problem investigated.     

A large-scale clustering and 3D trajectory optimization approach for UAV swarms

With the significant development of unmanned aerial vehicles (UAVs) technologies,a rapid increase on the use of UAV swarms in a wide range of civilian and emerg...     

Feed-rate and trajectory optimization for CNC machine tools

The key task performed by CNCs is the generation of the time-sequence of set-points for driving each physical axis of the machine tool during program execution. This interpolation of axes movement must satisfy a number of constraints on axes dynamics (velocity, acceleration, and jerk), and on process outcome (smooth tool movement and precise tracking of the nominal tool-path at the desired feed-rate). This paper presents an algorithm for CNC kernels that aims at solving the axes interpolation problem by exploiting an Optimal Control Problem formulation. With respect to other solutions proposed in the literature, the approach presented here takes an original approach by assuming a predefined path tracking tolerance—to be added to the constraints listed above—and calculating the whole trajectory (path and feed-rate profile) that satisfies the given constraints. The effectiveness of the proposed solution is benchmarked against the trajectory generated by an industrial, state-of-the-art CNC, proving a significant advantage in efficiency and smoothness of axes velocity profiles.     

An analogue technique for solving trajectory optimization problems

Analogue or hybrid computer methods for solving trajectory optimization problems usually require the solution of a two-point boundary value problem. A method of solving this problem is presented which does not require a good initial approximation to ensure convergence of the iteration. Analogue computer results are given with the method applied to a double integrator plant. Two different forms of performance index are considered.     

Advances in trajectory optimization for space vehicle control

Space mission design places a premium on cost and operational efficiency. The search for new science and life beyond Earth calls for spacecraft that can deliver scientific payloads to geologically rich yet hazardous landing sites. At the same time, the last four decades of optimization research have put a suite of powerful optimization tools at the fingertips of the controls engineer. As we enter the new decade, optimization theory, algorithms, and software tooling have reached a critical mass to start seeing serious application in space vehicle guidance and control systems. This survey paper provides a detailed overview of recent advances, successes, and promising directions for optimization-based space vehicle control. The considered applications include planetary landing, rendezvous and proximity operations, small body landing, constrained attitude reorientation, endo-atmospheric flight including ascent and reentry, and orbit transfer and injection. The primary focus is on the last ten years of progress, which have seen a veritable rise in the number of applications using three core technologies: lossless convexification, sequential convex programming, and model predictive control. The reader will come away with a well-rounded understanding of the state-of-the-art in each space vehicle control application, and will be well positioned to tackle important current open problems using convex optimization as a core technology.     

基于双层规划的自动卡车运输专用网络设计优化

为保障交通系统安全性和卡车货运自动化的发展,有必要对自动卡车货物运输专用网络进行科学规划与布局.考虑到自动卡车专用道会减少普通车辆的路权,对普通车辆的出行路径选择行为造成影响,首先从路网整体出发,以系统出行时间最小为目标,充分考虑路网普通车辆的出行路径选择行为,构建一种新的自动卡车专用运输网络设计的双层规划模型;然后提出一种基于实数编码的改进差分进化算法求解建立的双层规划模型,不仅保证解的可行性,还可避免复杂的不可行解修复过程.通过Sioux Falls基准网络实例和大量随机算例对比测试验证了所提出模型和算法的有效性.     

运输方式选择多目标优化问题的混合遗传算法

多式联运运输方式选择问题直接关系到货物运输的费用、时间和运输质量。首先分析了多式联运运输方式选择多目标优化问题的数学模型及虚拟运输网络图;其次,将基于信息熵的多属性决策方法引入适应度函数的设计中,提出了一种求解多式联运运输方式选择多目标优化问题的混合遗传算法,给出了染色体编码、遗传算子设计、染色体有效性判断和修正的方法;最后用示例对算法的有效性进行了验证。     

Cooperative particle swarm optimization for multiobjective transportation planning

The paper presents a multiobjective optimization problem that considers distributing multiple kinds of products from multiple sources to multiple targets. The problem is of high complexity and is difficult to solve using classical heuristics. We propose for the problem a hierarchical cooperative optimization approach that decomposes the problem into low-dimensional subcomponents, and applies Pareto-based particle swarm optimization (PSO) method to the main problem and the subproblems alternately. In particular, our approach uses multiple sub-swarms to evolve the sub-solutions concurrently, controls the detrimental effect of variable correlation by reducing the subproblem objectives, and brings together the results of the sub-swarms to construct effective solutions of the original problem. Computational experiment demonstrates that the proposed algorithm is robust and scalable, and outperforms some state-of-the-art constrained multiobjective optimization algorithms on a set of test problems.     

A co-simulation,optimization, control approach for traffic light control with truck priority

Under certain conditions giving priority to trucks at signalized intersections will benefit all vehicles because of elimination of extra delays associated with stop and go trucks due to their size and slow deceleration and acceleration rates. In this paper, we develop, analyse and evaluate a traffic light control system for signalized intersections that takes into account the differences in dynamics and characteristics between trucks and passenger vehicles. The proposed traffic light control system combines simulation-based optimization techniques timing the baseline traffic signals and model-based active strategy giving priority to trucks when it is to the benefit of all vehicles involved. In order to overcome the computational constraints of the simulation-based approach, a multiple agent-based solution utilizing multiple simulators has been proposed for large scale road network applications. The evaluation results show consistent improvements in reducing the truck traffic delays (5% to 10%) and the number of truck stops without delaying passenger vehicles whose travel time and number of stops have also been reduced. The reductions of vehicle delays and number of stops lead to reduction in emission levels and fuel consumption for both trucks and passenger vehicles.     

Ant colony optimization algorithms for solving transportation problems

The work is devoted to solving transportation problems with ant colony algorithms. These algorithms are based on the simulation of the behavior of an ant colony. Several modifications of the ant colony algorithm are developed.     

Control and optimization algorithms for air transportation systems

Modern air transportation systems are complex cyber-physical networks that are critical to global travel and commerce. As the demand for air transport has grown, so have congestion, flight delays, and the resultant environmental impacts. With further growth in demand expected, we need new control techniques, and perhaps even redesign of some parts of the system, in order to prevent cascading delays and excessive pollution.In this survey, we consider examples of how we can develop control and optimization algorithms for air transportation systems that are grounded in real-world data, implement them, and test them in both simulations and in field trials. These algorithms help us address several challenges, including resource allocation with multiple stakeholders, robustness in the presence of operational uncertainties, and developing decision-support tools that account for human operators and their behavior.     

Multi-objective cellular particle swarm optimization for wellbore trajectory design

Wellbore trajectory design is a determinant issue in drilling engineering. The criteria to evaluate a wellbore trajectory are summarized as the total trajectory length, the torque and the well profile energy in this paper. By minimizing the wellbore trajectory length, torque and profile energy simultaneously, it is most likely that a wellbore trajectory designed to arrive at the specific target can be drilled more safely, quickly and cheaply than other potential trajectories. However, these three objectives are often in conflict with each other and related in a highly nonlinear relationship. A multi-objective cellular particle swarm optimization (MOCPSO) with an adaptive neighborhood function is developed in this paper. Then, MOCPSO is applied with the three objective functions to gain a set of Pareto optimal solutions that are beneficial for a less risky and less costly wellbore trajectory design option. Besides, MOCPSO’s performance is compared with multi-objective PSO, multi-objective evolutionary algorithm based on decomposition (MOEA/D) and non-dominated sorting genetic algorithm-II (NSGA-II). Effect of the proposed neighborhood function is also investigated by making contrasts with the commonly used four neighborhood templates. Moreover, the radius parameter in the adaptive neighborhood function is analyzed to reveal its influence on the optimization performance. It can be inferred that MOCPSO is statistically superior to both multi-objective PSO, NSGA-II and MOEA/D at the 0.05 level of significance on the wellbore trajectory design problem. And the proposed adaptive neighborhood function performs either comparable or better as compared to the other commonly used neighborhood functions. According to the parameter analysis, it can be concluded that the MOCPSO approach with radius value of 1or 1.5 has a better statistical performance.