Electric Vehicle Charging Warning and Path Planning Method Based on Spark

Aiming at the problem of electric vehicle (EV) charging path selection under different road traffic conditions, an optimal charging navigation strategy for electric vehicle is proposed under the influence of traffic information. This strategy can make the electric car charging to get the best service in the shortest time; on this basis, a charging path guidance model is established, which takes full account of the time of electric vehicle driving path, the waiting time of charging station and the shortest charging time of electric vehicle charging and the shortest charging path as the objective function.. Based on Dijkstra algorithm, the experimental results show that the charging path planning strategy of the electric vehicle can plan reasonable and effective charging routes according to different road conditions. Compared with the traditional charging path planning strategy, this method can effectively reduce the total time cost, the total distance cost and improve the convenience of the user’s travel.

Path planning is one of the important directions in the field of intelligent vehicles research. Traditional path planning algorithms generally use Dijkstra algorithm, Breadth-First-Search (BFS) algorithm and A* algorithm. Dijkstra algorithm is a search-based algorithm, which can search to an optimal path, but the disadvantage is too many expansion nodes, which leads to insufficient search efficiency. BFS algorithm is a heuristic search algorithm, which reduces the disadvantage of too many expansion nodes and improves the search efficiency by heuristic function. A* algorithm is a heuristic search algorithm that combines Dijkstra’s algorithm and BFS algorithm, which has higher search efficiency and can search to an optimal path at the same time, but it is still lacking in the search mode and smoothness of the planned route. This paper first introduces the general path planning algorithm, then introduces and analyzes the A* algorithm, and proposes improvement measures for its shortcomings; finally, the executability and effectiveness of the improved algorithm are tested using simulation, and compared with the traditional A* algorithm, and the results show that the improved A* algorithm has good effect on path planning of intelligent vehicles.

Many EU countries have used financial incentives to promote electric light commercial vehicles and decarbonise urban freight transport. However, the high costs of electric light commercial vehicles still pose a significant adoption barrier. This paper evaluates the impact of financial incentives on the total cost of ownership of electric light commercial vehicles in the European Union and their role in achieving the cost-competitiveness of electric vehicles. A pairwise comparison is performed for three weight categories of electric and diesel vehicles in twelve countries. Our analysis shows that financial incentives have a high impact on the total cost of ownership and have proven necessary to achieve electric vehicles' cost-competitiveness in most cases. However, in specific cases, financial incentives may no longer be needed as the cost of electric vehicles' ownership would have been lower than diesel vehicles even without them. This paper demonstrates the importance of adjusting government incentives to avoid unnecessary funding allocations while promoting the decarbonisation of light commercial vehicles, particularly as the number of incentives does not directly correlate with electric vehicles penetration.

This paper presents a survey of the most recent literature developments in the field of computational guidance and control for space applications. In recent years, the space sector has changed significantly, shifting towards solutions requiring increased performance and autonomy. This change has also been accelerated by the technical and economic needs of the new space approach. However, traditional guidance and control techniques cannot always meet the increasing requirements of autonomy and performance. For this reason, computational guidance and control has become of increasing interest to the aerospace sector. In contrast to traditional guidance and control, computational guidance and control generates commands in real time by using on-board numerical computation. It represents, therefore, a fundamental step on the way to system autonomy and autonomous operations, with the potential to increase system capabilities and reduce operational costs. On-board computation introduces, however, a series of challenges, including the need to develop algorithms which are computationally efficient, reliable and robust. The literature has classified computational guidance and control techniques into two major categories, namely model-based and data-based. The model-based category encompasses all techniques in which the guidance and control laws are explicitly related to the system dynamic and kinematic models. On the contrary, data-based techniques infer guidance and control laws from a set of data, derived from the models, but do not explicitly use such models in the algorithm itself. Model-based guidance and control includes optimal control, model predictive control and convex optimisation. For the specific case of space attitude re-orientation manoeuvres, additional techniques are path planning and artificial potential functions. The general concept behind the Model Predictive Control (MPC) framework is to solve an optimal control problem repeatedly, for example whenever new measurements are available. The MPC implements only the first element of the returned solution, and the process is then repeated. By doing so, the system is better able to cope autonomously with model uncertainties, failures, or errors. For spacecraft with limited on-board capability, having to repeatedly solve an optimisation problem on-board could represent a challenge. In this case, explicit implementations of the MPC have been proposed, in which the guidance and control laws are precomputed offline and implemented on-board using look-up tables or simple functions. If the optimal control problem of the MPC is convex, or if it can be convexified, convex optimisation can be used directly. If the problem cannot be convexified, the solution can be found by sequentially solving a convex approximation of the original non-convex problem. Convex optimisation can be used either repeatedly in the context of an MPC framework, or as a single function call to generate guidance and control laws, e.g. at the beginning of an attitude re-orientation manoeuvre. Convex optimisation has received increased attention in recent years, also due to its suitability to on-board implementation. In fact, if the optimal control problem is convex, it can be solved reliably and efficiently on-board. Convex optimisation has been successfully applied to the space sector with the notable examples of the SpaceX rocket booster landing and of the Mars pinpoint landing demonstration aboard the Masten Xombie sounding rocket. Model-based guidance techniques, not based on optimisation, have been increasingly explored in recent years, as guidance strategies employed in other engineering fields, such as robotics, are being applied to spacecraft. Among these strategies, of particular importance are path planning and artificial potential field methods, due to their applicability to spacecraft autonomous attitude manoeuvres. Path planning algorithms have been applied for decades to ground vehicles and robotic manipulators. These algorithms consist of techniques to find the best route between two points of a graph representing the search space of a problem. For spacecraft re-orientation applications, the literature has proposed different attitude representation conventions and various cost functions associated to the graph search algorithm. Each approach comes with advantages and disadvantages, which will be analysed in the paper. Along with path planning, Artificial Potential Function (APF) techniques have been developed in the field of robotics, and are now also considered for space applications, to track or maintain a specific reference under a set of constraints. The main idea behind these methods is to synthetise the controller using a potential function. This function is specifically built to grant asymptotic stability to the system, according to Lyapunov’s second stability theorem. This formulation grants flexibility to accommodate system constraints, in a computationally light-weight and efficient manner. Data-based guidance and control laws are mainly based on Artificial Intelligence (AI) technologies, and generate guidance commands after training an algorithm with a large set of data. The interest of the space sector towards AI has accelerated in the last years due to the greater maturity of the technology. Some of the most powerful examples lie in the area of machine learning, like reinforcement learning, supervised learning and deep learning. These algorithms are used to extract patterns from large amounts of data derived from complex nonlinear dynamical and kinematical models. Examples of such data can be numerical solutions of an optimal control problem. The patterns extracted from the data allow the system to identify, in a short computational time, the critical parameters needed to relate the state and control variables. Creating a sufficiently large dataset for training the algorithm requires a considerable computational effort, though, and for certain methods optimality cannot be guaranteed due to the lack of theoretical support derived from the optimality conditions. Advancements made in the last years span several research directions and open a wide variety of opportunities and challenges, which need to be examined to guide further exploration. In this paper, both model-based and data-based techniques and applications are presented. The considered techniques include model predictive control, convex optimisation and machine learning. In addition, for the implementation of autonomous attitude manoeuvres, path planning methods and artificial potential function methods are analysed. These techniques have been applied, in the literature, to a range of different applications. The applications discussed in this paper include, in addition to attitude manoeuvres, powered descent landing guidance, low-thrust orbital transfers, proximity operations and station keeping.

In this paper a novel combination of data structures and algorithms is presented as a fast and efcient practical approach to path planning for unmanned vehicles. This approach utilizes an architecture that integrates effective obstacle representation using an efcient Quadtree data structure, with path planning based on a modied Rapidly-exploring Random Tree (RRT) algorithm, and a path pruning technique based on Dijkstra’s algorithm, resulting in rapid discovery of sub-optimal feasible paths. One of the computationally expensive steps of the RRT algorithm is checking RRT segments for collision with obstacles. To address this problem, an efcient obstacle representation using Quadtrees is implemented enabling fast segment checks. While the RRT approach is guaranteed to nd a feasible path through complex obstacle elds with the probability of discovery approaching one as the number of iterations increases, the resulting paths may be far from optimal as the only criterion embedded in the RRT search is feasibility. For this reason, the paths generated by the RRT algorithm are passed through Dijkstra algorithm that nds a shortest obstacle-free path among the points on the path. Dijkstra algorithm that re-samples the segments of the path and nds the shortest path among the new points is implemented resulting in paths that are very close to the optimal ones. To speed up the computation, the basic search algorithm is implemented as a dual RRT from the initial and nal points. To account for motion transients along the path, a suitable size corridor is introduced, assuring that the vehicle has enough room for maneuvering among the obstacles. This computationally efcient architecture lends itself to real-time implementation for both pre-planning with known obstacles as well as rapid reactive re-planning in the presence of new pop-up obstacles. The proposed technique is evaluated using several challenging path-planning examples characterized by a large number of non-convex constraints arising in cluttered obstacle elds with known and unknown obstacles. A modication that allows an easy way to introduce vehicle dynamics constraints or actuation failures is also implemented. Algorithms have been implemented for two as well as three dimensional path planning applications.

An agent-based simulation study for escaping the “chicken-egg” dilemma between electric vehicle penetration and charging infrastructure deployment

This paper is introduced to be the obstacle recognition, path planning and oscillating control for an automobile crane system on-line. We studied the autonomous mobile overhead traveling crane so for. In particular, the research of obstacle recognition, path plan and oscillating control is carried out. For on-line obstacle recognition, the obstacle recognition system using the ultrasonic sensor(USS) is built. And on-line partial obstacle recognition of the conveyance environment is carried out using this obstacle recognition system. For an on-line path planning, the path plan system which it could process with the information of transfer object position and the obstacle position that is recognized by obstacle recognition system in a short time was built. The feedback transfer control system in consideration of oscillating control is built. Finally, the usefulness of the system that these were integrated is proved by an experiment. Keywords-component; Automation, Mobile Robot, Overhead Traveling Crane, On-line Path Planning, Oscillating control. I. INTORODUCTION OF THIS PAPER An overhead traveling crane system has an advantage of selecting 3rd-dimentionally and freely a transfer path achieving a freedom thereof. Therefore, automation and autonomy thereof is desired to increase the operational freedom of FMS(Flexible Manufacturing System). In the present, the overhead traveling crane is mainly operated by an expert operator to ensure safety during transferring. The development of an autonomous mobile overhead traveling crane is necessary for problem of the lack of skillful operators or the problem of work efficiency. For the reason, the results of research about an autonomous mobile overhead traveling crane system are presented by us[1],[2],[4],[5]. In particular, our research for path planning was conducted from various points of view[1],[2],[4],[5]. In a past research, we has made an experimental apparatus of on overhead traveling crane shown in Fig.1, and an autonomous mobile traveling crane system with function of the obstacle recognition, path planning on-line and oscillation control has been constructed. In this visual and planning system, when there was the change of an obstacle conveying, it was necessary for the path plan to be handled in a short time and had a problem with on-line processing in a conventional study. And the path planning system is constructed[2], [4],[5]. We propose the path-planning system that can be quickly carried out with an easy algorithm on-line. The proposed online path planning system is built using two-dimensions pathplanning algorithm[3] to perform a three-dimensional path planning[4],[5]. In this proposed system, path planning can be derived by information of target position and any obstacle position that is recognized by an ultrasonic sensor (USS). This on-line path plan method performs a path plan by the partial information of a transportation environment recognized by USS of the obstacle recognition system during transfer. The path plan of the overhead traveling crane is constructed to the goal position by repeating a suggested process (algorithm). Furthermore, the inspection of the proposed path planning method was evaluated from a view point of a qualitative and a quantitative[5]. In the present condition, the transfer control system in consideration of oscillating control was a feed-forward control system which using the notch filter. Therefore this control system had the problem that oscillating control was not completed when there was remains vibration at the time of change of a path. In this paper, 2 degrees of freedom control system which added the feedback control system to the present control system are designed. And the purpose of this research is to verify the usefulness of the on-line obstacle avoidance of the proposed autonomous mobile overhead traveling crane system. 2nd International Symposium on Computer, Communication, Control and Automation (3CA 2013) © 2013. The authors Published by Atlantis Press 468 Fig. 2 Obstacle recognition system

Based on the characteristic of autonomous underwater vehicle path planning, the method of path planning was analyzed by genetic algorithm. Firstly, by means of grid, plan space was modeled into two markers. Best path was searched by genetic algorithm. Method of giving birth to initial groups was improved. Sufficiency function of path planning was given. Chamfer operator in genetic algorithm was imported to speed up the convergence. It guarantees the request of planning time. Through simulation experiment, the important function of chamfer operator was proved in path planning. The method can produce an almost best path for Autonomous underwater vehicle in short time.

Recent Advances in Mobile Robotics 128 gradient field Unfortunately, path planning approach based on the function family of potential field cannot obtain the optimal objective function which, in turn, cannot guarantee the desired optimal trajectories. Additionally, some of them, for example, potential panel method and harmonic potential function, can plan a path for an autonomous vehicle, but the computing burdens is huge and real time performance hardly satisfies the practical requirement. Inspired by biological intelligence, many approaches, such as ant colony optimization (ACO) They mostly rely on the stochastic searching, known as non-deterministic algorithm. These methods will eventually find an optimal solution, but no estimate on the time of convergence can be given. Thus, it may take long even infinite time to find the best solution. Furthermore, all of them have a great number of parameters to tune and that is never an easy job particularly when users are short of prior knowledge. Some comparisons Another kind of method is mathematic programming. Based on the relative velocity coordinates (RVCs), linear programming (LP) and mixed integer linear programming (MILP) can be employed for path planning problem. For example, Wang et al. converted the 2D path planning of an unmanned underwater vehicle to constrained optimization or semiinfinite constrained optimization problem Zu et al. discussed the path planning in 2D and an LP method was proposed for the problem of dynamic target pursuit and obstacle avoidance This method tried to plan the variables of the linear acceleration and the angular acceleration of a ground vehicle. Schouwenaars et al. proposed a MILP formulation with receding horizon strategy where a minimum velocity and a limited turn rate of aircraft are constrained However, these results still focused on the two dimensions. In this chapter, we consider the same problem of TPOA but in three dimensions. To our problem, the uncertain environment has one target or some of them, denoted by G, and many obstacles O that are all velocity-changeable with their moving actions. The aim of the vehicle A is to find an optimal path for pursuing the target while, at the same time, avoiding collision threaten from obstacles. The position and velocity of movers, including the target and obstacles, are assumed known or estimated at current time. To be more specific, it is assumed that the noise contained in the data can be eliminated with certain filters. However, this paper has no intend to discuss the filtering algorithm for achieving the feasible data of on-board sensors. The trajectory from a start to a destination location typically needs to be computed gradually over time in the fashion of receding horizon The target and obstacles, probably static or dynamic, are modeled by spheres having a certain radius for their impact area, and the vehicle is modeled by a mass point. In order to optimize the vehicle's acceleration online, we construct a linear programming model in Cartesian orthogonal coordinates based on relative velocity space This chapter is organized as follows. First, the relative velocity coordinates are introduced in both two dimensions and three dimensions and then the TPOA principles are introduced, including obstacle-avoidance and target-pursuit principles. The formulation of the standard linear programming is mathematically introduced in Section 3.1, and the path planning model is described in detail in Section 3.2. Simulations of path planning in 2D and 3D are www.intechopen.com Path Planning of Mobile Robot in Relative Velocity Coordinates 129 A, G, O Vehicle, target, obstacle Planning period

This study aims to solve path planning of intelligent vehicles in self-driving. In this study, an improved path-planning method combining constraints of the environment and vehicle is proposed. The algorithm designs a reasonable path cost function, then uses a heuristic guided search strategy to improve the speed and quality of path planning, and finally generates smooth and continuous curvature paths based on the path post-processing method focusing on the requirements of path smoothness. A simulation test shows that compared with the basic rapidly-exploring random tree (RRT), RRT-Connect and RRT* algorithms, the path length of the proposed algorithm can be reduced by 19.7%, 29.3% and 1% respectively, and the maximum planned path curvature of the proposed algorithm is 0.0796 m-1 and 0.1512 m-1 respectively, under the condition of a small amount of planning time. The algorithm can plan the more suitable driving path for intelligent vehicles in a complex environment.

Recent advancements have been made in unmanned freight systems at ports, effectively improving port freight efficiency and being widely promoted and popularized in the field of cargo transportation in major ports around the world. The path planning and motion control of port transfer vehicles are the key technology for automatic transportation of vehicles. How to integrate cutting-edge unmanned driving control technology into port unmanned freight transportation and improve the level of port automation is currently an important issue. This article introduces the three-layer control operation architecture of unmanned freight systems in ports, as well as the challenges of applying path planning and motion control technology for unmanned freight vehicles in port environments. It focuses on the mainstream algorithms of path planning and motion control technology, introduces their principles, provides a summary of their current development situation, and elaborates on the improvement and integration achievements of current researchers on algorithms. The algorithms are reviewed and contrasted, highlighting their respective strengths and weaknesses. Finally, this article looks ahead to the development trend of unmanned cargo transportation in ports and provides reference for the automation and intelligent upgrading of unmanned cargo transportation in ports in the future.

Due to increasing peak power demand and decreasing quality of power delivery to consumers, the penetration of Electric Vehicles (EVs) are highly acceptable for grid support. If the EVs are exchange active and reactive power to grid during parking hours, then the peak power demand has reduced and quality of power delivery has improved. The EVs are discharged at a place to be called as a Grid Supporting Station (GSS). The GSS have multiple Discharging Units (DUs) which transfers active and reactive power from EVs' batteries to grid. In this paper, a fuzzy based control method is described for EVs which regulate the active and reactive power for grid support. The active and reactive power flow from EVs to grid based on the node voltage and amount of energy available in the GSS. Each DU is designed for a maximum power handling capacity of 80VA. The performance of the GSS is analyzed with five set of EVs which is connected through point of Common Coupling (PCC) of distribution node. Simulation studies show that the GSS could effectively exchange the power for grid support during the peak hours.

A cuspidal robot can move from one inverse kinematics (IK) solution to another without crossing a singularity. Multiple industrial robots are cuspidal. They tend to have a beautiful mechanical design, but they pose path-planning challenges. A task-space path may have a valid IK solution for each point along the path, but a continuous joint-space path may depend on the choice of the IK solution or even be infeasible. This article presents new analysis, path planning, and optimization methods to enhance the utility of cuspidal robots. We first demonstrate an efficient method to identify cuspidal robots and show, for the first time, that the ABB GoFa and certain robots with three parallel joint axes are cuspidal. We then propose a new path-planning method for cuspidal robots by finding all IK solutions for each point along a task-space path and constructing a graph to connect each vertex corresponding to an IK solution. Graph edges have a weight based on the optimization metric, such as minimizing joint velocity. The optimal feasible path is the shortest path in the graph. This method can find nonsingular paths as well as smooth paths that pass through singularities. Finally, we incorporate this path-planning method into a path optimization algorithm. Given a fixed workspace toolpath, we optimize the offset of the toolpath in the robot base frame while ensuring continuous joint motion. Code examples are available in a publicly accessible repository.

Maritime transportation is vital to the global economy. With the increased operating and labor costs of maritime transportation, autonomous shipping has attracted much attention in both industry and academia. Autonomous shipping can not only reduce the marine accidents caused by human factors but also save labor costs. Path planning is one of the key technologies to enable the autonomy of ships. However, mainstream ship path planning focuses on searching for the shortest path and controlling the vehicle in order to track it. Such path planning methods may lead to a dynamically infeasible trajectory that fails to avoid obstacles or reduces fuel efficiency. This paper presents a data-driven, efficient, and safe path planning (ESP) method that considers ship dynamics to provide a real-time optimal trajectory generation. The optimization objectives include fuel consumption and trajectory smoothness. Furthermore, ESP is capable of fast replanning when encountering obstacles. ESP consists of three components: (1) A path search method that finds an optimal search path with the minimum number of sharp turns from the geographic data collected by the geographic information system (GIS); (2) a minimum-snap trajectory optimization formulation with dynamic ship constraints to provide a smooth and collision-free trajectory with minimal fuel consumption; (3) a local trajectory replanner based on B-spline to avoid unexpected obstacles in real time. We evaluate the performance of ESP by data-driven simulations. The geographical data have been collected and updated from GIS. The results show that ESP can plan a global trajectory with safety, minimal turning points, and minimal fuel consumption based on the maritime information provided by nautical charts. With the long-range perception of onboard radars, the ship can avoid unexpected obstacles in real time on the planned global course.

PurposeThis paper aims to present a comprehensive review in major research areas of unmanned air vehicles (UAVs) navigation, i.e. three degree-of-freedom (3D) path planning, routing algorithm and routing protocols. The paper is further aimed to provide a meaningful comparison among these algorithms and methods and also intend to find the best ones for a particular application.Design/methodology/approachThe major UAV navigation research areas are further classified into different categories based on methods and models. Each category is discussed in detail with updated research work done in that very domain. Performance evaluation criteria are defined separately for each category. Based on these criteria and research challenges, research questions are also proposed in this work and answered in discussion according to the presented literature review.FindingsThe research has found that conventional and node-based algorithms are a popular choice for path planning. Similarly, the graph-based methods are preferred for route planning and hybrid routing protocols are proved better in providing performance. The research has also found promising areas for future research directions, i.e. critical link method for UAV path planning and queuing theory as a routing algorithm for large UAV networks.Originality/valueThe proposed work is a first attempt to provide a comprehensive study on all research aspects of UAV navigation. In addition, a comparison of these methods, algorithms and techniques based on standard performance criteria is also presented the very first time.