Trajectory planning for unmanned aerial vehicles: a network optimization approach

This paper presents a new approach for trajectory planning of air vehicles. It considers scenarios with risk areas and forbidden zones and takes into account the maneuverability of the air vehicle. It is flexible as to allow different kinds of objective functions such as minimizing risk, flight path length or flight time, and allows to implement constraints on fuel consumption or other resources. Additionally, it can incorporate waypoints to be passed by the air vehicle with or without specified overflight directions. The method includes planning of one-way and return trips. The underlying model is based on a discretization of the airspace into a non-regular network. Every path in the network corresponds to a flyable trajectory which means that the trajectory is within the performance limits of the air vehicle. The generation of the network is done non-deterministically. One of the main benefits of the model is that one can make use of standard network optimization techniques.     

Path planning for robots under stochastic uncertainty *

In order to reduce large online measurement and correction expenses, the a priori informations on the random variations of the model parameters of a robot and its working environment are taken into account already at the planning stage. Thus, instead of solving a deterministic path planning problem with a fixed nominal parameter vector, here, the optimal velocity profile along a given trajectory in work space is determined by using a stochastic optimization approach. Especially, the standard polygon of constrained motion-depending on the nominal parameter vector-is replaced by a more general set of admissible motion determined by chance constraints or more general risk constraints. Robust values (with respect to stochastic parameter variations) of the maximum, minimum velocity, acceleration, deceleration, resp., can be obtained then by solving a univariate stochastic optimization problem Considering the fields of extremal trajectories, the minimum-time path planning problem under stochastic uncertainty can be solved now by standard optimal deterministic path planning methods     

A methodology to solve large-scale cooperative transportation planning problems

In this paper, we suggest a methodology to solve a cooperative transportation planning problem and to assess its performance. The problem is motivated by a real-world scenario found in the German food industry. Several manufacturers with same customers but complementary food products share their vehicle fleets to deliver their customers. After an appropriate decomposition of the entire problem into sub problems, we obtain a set of rich vehicle routing problems (VRPs) with time windows for the delivery of the orders, capacity constraints, maximum operating times for the vehicles, and outsourcing options. Each of the resulting sub problems is solved by a greedy heuristic that takes the distance of the locations of customers and the time window constraints into account. The greedy heuristic is improved by an appropriate Ant Colony System (ACS). The suggested heuristics to solve the problem are assessed within a dynamic and stochastic environment in a rolling horizon setting using discrete event simulation. We describe the used simulation infrastructure. The results of extensive simulation experiments based on randomly generated problem instances and scenarios are provided and discussed. We show that the cooperative setting outperforms the non-cooperative one.     

Aerial Vehicle Search-Path Optimization: A Novel Method for Emergency Operations

This paper presents a novel search-path optimization method for moving target search by an aerial vehicle, applicable to realistically sized search areas. For such missions, long endurance vehicles are needed, which are usually fixed-winged. The proposed method accounts for flight kinematics of fixed-wing and rotary-wing aerial vehicles. It additionally accounts for movements of the target, considerably increasing complexity of search-path optimization, compared to a static target. The objective is to maximize the probability to detect a conditionally deterministic moving target within a given time period. We propose a first K-step-lookahead planning method that takes flight kinematic constraints into account and in which the target and platform state space are heterogeneous. It consists of a binary integer linear program that yields a physically feasible search-path, while maximizing the probability of detection. It is based on the Max-K-Coverage problem, as it selects K waypoints while maximizing the probability that a target is within the field of view of a platform at one of these waypoints. This K-step-lookahead planning method is embedded in an iterative framework, where the probability of overlooking a target is fed back to the controller after observations are made. Simulations show the applicability and effectiveness of this method.     

An APF and MPC combined collaborative driving controller using vehicular communication technologies

Collaborative driving is a growing domain of Intelligent Transportation Systems (ITS) which aim to navigate traffic both efficiently and safely. Cooperation between vehicles heavily rely on the comprehensive information collected. With the development of vehicular communication technologies, information can be shared between vehicles or infrastructures through Vehicle-to-Vehicle (V2V)/Vehicle-to-Infrastructure (V2I) data exchange. By taking advantage of data sharing between vehicles, this paper proposes an Artificial Potential Field (APF) and Model Predictive Control (MPC) combined controller to implement collaborative driving in complex environments. Firstly, an APF model ​containing three components is developed to describe the mutual effect and collaboration properties between vehicles and surrounding environments. Afterwards, a MPC cost function for optimized control, considering both kinematic characteristics and environmental effect conveyed by APF, is presented to address the problem of collaborative driving. Such controller is designed from the perspective of multi-objective and multi-constraint optimization which takes the vehicle motion constraints, safety and comfort requirements into consideration. The prominent advantage of the proposed approach is that it can deal with the problems of route planning and manipulating simultaneously. To validate the proposed approach, a variety of scenario simulations are conducted in MATLAB, and the performance of the proposed method are verified.     

Modeling target volume flows in forest harvest scheduling subject to maximum area restrictions

In forest harvest scheduling problems, one must decide which stands to harvest in each period during a planning horizon. A typical requirement in these problems is a steady flow of harvested timber, mainly to ensure that the industry is able to continue operating with similar levels of machine and labor utilizations. The integer programming approaches described use the so-called volume constraints to impose such a steady yield. These constraints do not directly impose a limit on the global deviation of the volume harvested over the planning horizon or use pre-defined target harvest levels. Addressing volume constraints generally increases the difficulty of solving the integer programming formulations, in particular those proposed for the area restriction model approach. In this paper, we present a new type of volume constraint as well as a multi-objective programming approach to achieve an even flow of timber. We compare the main basic approaches from a computational perspective. The new volume constraints seem to more explicitly control the global deviation of the harvested volume, while the multi-objective approach tends to provide the best profits for a given dispersion of the timber flow. Neither approach substantially changed the computational times involved.     

Optimal planning of network structures within an exchange area

The cost-optimal planning and capacity extension of a given network structure is described by a mixed-integer program. The decision variables in this model are first discrete digital systems (PCM) set up on existing cable lines and secondly new cable links required in the future. Furthermore, this formulation takes account of the circuit capacity of the system and path diversification required for reliability reasons. This planning model describes a real-world problem that can be formulated not only mathematically as a true reproduction of reality but can also be solved in a closed form within reasonable computer time. Opposite to the operational planning methods, this model allows a global cost-optimal network to be obtained, which is subject to a certain set of constraints.     

An arc cover–path-cover formulation and strategic analysis of alternative-fuel station locations

In this study, we present a new formulation of the generalized flow-refueling location model that takes vehicle range and trips between origin–destination pairs into account. The new formulation, based on covering the arcs that comprise each path, is more computationally efficient than previous formulations or heuristics. Next, we use the new formulation to provide managerial insights for some key concerns of the industry, such as: whether infrastructure deployment should focus on locating clusters of facilities serving independent regions or connecting these regions by network of facilities; what is the impact of uncertainty in the origin–destination demand forecast; whether station locations will remain optimal as higher-range vehicles are introduced; and whether infrastructure developers should be willing to pay more for stations at higher-cost intersections. Experiments with real and random data sets are encouraging for the industry, as optimal locations tend to be robust under various conditions.     

Solving real-life vehicle routing problems efficiently using tabu search

This paper presents a tabu search based method for finding good solutions to a real-life vehicle routing problem. The problem considered deals with some new features beyond those normally associated with the classical problems of the literature: in addition to capacity constraints for vehicles and time windows for deliveries, it takes the heterogeneous character of the fleet into account, in the sense that utilization costs are vehicle-dependent and that some accessibility restrictions have to be fulfilled. It also deals with the use of trailers. In spite of the intricacy of the problem, the proposed tabu search approach is easy to implement and can be easily adapted to many other applications. An emphasis is placed on means that have to be used to speed up the search. In a few minutes of computation on a personal workstation, our approach obtains solutions that are significantly better than those previously developed and implemented in practice.     

Adapting the ng-path relaxation for bike balancing problems

This paper deals with a static bike relocation problem that deploys a fleet of vehicles to redistribute shared bicycles. To solve the problem to optimality, we present a branch-price-and-cut algorithm. In particular, a new path relaxation for the pricing problem is introduced that relaxes the constraints on the maximum number of bikes to move at each station in a similar fashion as elementary can be relaxed in vehicle routing problems. Computational results show that our algorithm outperforms the former state-of-the-art.     

Optimal ship navigation with safety distance and realistic turn constraints

We consider the optimal ship navigation problem wherein the goal is to find the shortest path between two given coordinates in the presence of obstacles subject to safety distance and turn-radius constraints. These obstacles can be debris, rock formations, small islands, ice blocks, other ships, or even an entire coastline. We present a graph-theoretic solution on an appropriately-weighted directed graph representation of the navigation area obtained via 8-adjacency integer lattice discretization and utilization of the A^∗ algorithm. We explicitly account for the following three conditions as part of the turn-radius constraints: (1) the ship’s left and right turn radii are different, (2) ship’s speed reduces while turning, and (3) the ship needs to navigate a certain minimum number of lattice edges along a straight line before making any turns. The last constraint ensures that the navigation area can be discretized at any desired resolution. Once the optimal (discrete) path is determined, we smoothen it to emulate the actual navigation of the ship. We illustrate our methodology on an ice navigation example involving a 100,000 DWT merchant ship and present a proof-of-concept by simulating the ship’s path in a full-mission ship handling simulator.     

Covering and connectivity constraints in loop-based formulation of material flow network design in facility layout

The shortest loop covering at least one edge of each workcenter in a manufacturing facility layout is an instance of the generalized traveling salesman problem. The optimal solution to this problem is a promising design for non-vehicle-based material handling, typical of most types of conveyors and power-and-free systems, where the length of the path is the main driver of the total investment costs. The loop formulation is usually embedded within a larger problem of the concurrent design of the loop and the input/output stations for vehicle-based material handling typical of automatically guided vehicles and autonomous delivery robots. In these systems, it is not the length, but the total flow of the loaded and empty vehicles that drives the objective function. It has been shown that the shortest loop provides an effective heuristic scheme to achieve prosperous and robust solutions for the concurrent design of the loop and input/output stations. We review and compare covering constraints formulations, provide new insight into connectivity constraints, improve the model formulation and its solution procedure, and report computational results.     

Budget and long-range planning in public transportation — An applicative linear programming model

Economic and financial planning is an actual problem for Italian Transport Authorities, since in Italy there are scarce financial resources, to cover either the difference between costs and fares proceeds or investment needs. The proposed model is ‘just tailored’ on public transport, subjected to the Italian laws; it takes into account the particular activities (‘functions’) connected with public transport operating (e.g.: fare collection, service production, maintenance, purchases, inventories, administration). Each function is considered as a ‘module’ with its own constraints, and it is connected with the other ‘modules’, so that we can get a ‘representative’ model. Also the most significant parameters of the ‘public transport operating problem’ (e.g.: vehicle miles, passenger journeys, number of vehicles, number of employees, and so on) are connected one another. Since all relations and constraints, connecting the ‘problem variables’ can be represented as linear, the model structure is based on linear programming; this fact allows to pursue an optimum for one or more objective functions, each of them identifing an operational policy. So it is possible to connect simulation with optimization. A multi-period model can be used for long-range planning, pursuing a multi-year optimum; this is the most significant use. The Administrative Module contains Balance-Sheet, Cash-Flow, Profit and Loss Account, which are ‘constraints’ in the model and are expressed as Italian financial laws require; this structure is one of the main characteristics. Modules' dimensions and additional constraints (e.g. economic or financial ratios, fare level, turnover of inventories, and so on) can be ‘tailored’ on each particular case, driving the model towards more realistic solutions.     

Dual-Sampling-Rate Moving-Horizon Control of a Class of Linear Systems with Input Saturation and Plant Uncertainty

Moving-horizon control is a type of sampled-data feedback control in which the control over each sampling interval is determined by the solution of an open-loop optimal control problem. We develop a dual-sampling-rate moving-horizon control scheme for a class of linear, continuous-time plants with strict input saturation constraints in the presence of plant uncertainty and input disturbances. Our control scheme has two components: a slow-sampling moving-horizon controller for a nominal plant and a fast-sampling state-feedback controller whose function is to force the actual plant to emulate the nominal plant. The design of the moving-horizon controller takes into account the nonnegligible computation time required to compute the optimal control trajectory.We prove the local stability of the resulting feedback system and illustrate its performance with simulations. In these simulations, our dual-sampling-rate controller exhibits performance that is considerably superior to its single-sampling-rate moving-horizon controller counterpart.     

点带约束成本的最短路问题

本文提出了点带约束成本的最短路问题,证明了该问题是ＮＰ－完全的,并利用动态规划给出了一个伪多项式算法,对所有顶点约束成本相同的情况,给出了一个时间复杂性为Ｏ（ｍｎ＾２）的算法,对最小点成本最短路问题,给出了一个时间复杂性为Ｏ（ｎ＾２）的算法。     

Probabilistic programming models for traffic incident management operations planning

This paper proposes mathematical programming models with probabilistic constraints in order to address incident response and resource allocation problems for the planning of traffic incident management operations. For the incident response planning, we use the concept of quality of service during a potential incident to give the decision-maker the flexibility to determine the optimal policy in response to various possible situations. An integer programming model with probabilistic constraints is also proposed to address the incident response problem with stochastic resource requirements at the sites of incidents. For the resource allocation planning, we introduce a mathematical model to determine the number of service vehicles allocated to each depot to meet the resource requirements of the incidents by taking into account the stochastic nature of the resource requirement and incident occurrence probabilities. A detailed case study for the incident resource allocation problem is included to demonstrate the use of proposed model in a real-world context. The paper concludes with a summary of results and recommendations for future research.     

Even-aged restrictions with sub-graph adjacency

Restrictions on the size and proximity of clearcuts have led to the development of a variety of exact and heuristic methods to optimize the net present value of timber harvests, subject to adjacency constraints. Most treat harvest units as pre-defined, and impose adjacency constraints on any two units sharing a common border. By using graph theory notation to define sub-graph adjacency constraints, opening size can be considered variable, which may be more appropriate for landscape-level planning. A small example data set is used in this paper to demonstrate the difference between the two types of adjacency constraints for both integer programming and heuristic solution methods.     

Omnidirectional walking of legged robots with a failed leg

This paper studies omnidirectional walking of a hexapod robot with a locked joint failure by proposing crab gaits and turning gaits. Due to the reduced workspace of a failed leg, fault-tolerant gaits have limitations in their mobility. As for crab gaits, an accessible range of the crab angle is derived for a given configuration of the failed leg. As for turning gaits, the conditions on turning trajectories guaranteeing fault tolerance are derived for spinning gaits and circling gaits. Based on the principles of fault-tolerant gait planning, periodic crab gaits and turning gaits are proposed in which a hexapod robot realizes tripod walking after a locked joint failure, having a reasonable stride length and stability margin. The proposed fault-tolerant gaits are then applied to an obstacle avoidance problem of a hexapod robot with a locked joint failure. The kinematic constraints of fault-tolerant gaits should be considered in planning the robot trajectory.     

面向电商终端物流配送的电动车配置与路径集成优化

在电子商务终端物流配送方面,存在能力与需求的矛盾。一方面,电动车存在货物容量约束和电池电量约束,配送能力有限;另一方面,一个物流配送点需要为众多的消费者进行门到门的配送,配送任务繁重。针对电子商务环境下终端物流配送规模大、电动车货物容量和行驶里程有限的问题,建立电商终端物流配送的电动车配置与路径规划集成优化模型,并提出一种基于临近城市列表的双策略蚁群算法,实现物流配送电动车辆配置与配送路径集成优化。该模型以电动车辆数最少和总路径最短为目标,以电动车货物容量和电池续航里程为约束,是带容量的车辆路径问题的进一步扩展,属于双容量约束路径规划问题。双策略蚁群算法在货物容量和续航里程的约束下,将蚁群搜索策略分为两类,即基于临近城市列表的局部搜索策略和全局搜索策略,在提高搜索效率的同时防止陷入局部优化。最后,通过阿里巴巴旗下菜鸟网络科技有限公司在上海的30组真实配送数据进行了测试,验证双策略蚁群算法显著优于一般蚁群算法。     

A Hybrid Multiobjective Evolutionary Algorithm for Solving Vehicle Routing Problem with Time Windows

Vehicle routing problem with time windows (VRPTW) involves the routing of a set of vehicles with limited capacity from a central depot to a set of geographically dispersed customers with known demands and predefined time windows. The problem is solved by optimizing routes for the vehicles so as to meet all given constraints as well as to minimize the objectives of traveling distance and number of vehicles. This paper proposes a hybrid multiobjective evolutionary algorithm (HMOEA) that incorporates various heuristics for local exploitation in the evolutionary search and the concept of Pareto's optimality for solving multiobjective optimization in VRPTW. The proposed HMOEA is featured with specialized genetic operators and variable-length chromosome representation to accommodate the sequence-oriented optimization in VRPTW. Unlike existing VRPTW approaches that often aggregate multiple criteria and constraints into a compromise function, the proposed HMOEA optimizes all routing constraints and objectives simultaneously, which improves the routing solutions in many aspects, such as lower routing cost, wider scattering area and better convergence trace. The HMOEA is applied to solve the benchmark Solomon's 56 VRPTW 100-customer instances, which yields 20 routing solutions better than or competitive as compared to the best solutions published in literature.