Analysis of Ice and Snow Path Planning System Based on MNN Algorithm

With the development of robotics and artificial intelligence field unceasingly thorough, path planning as an important field of robot calculation has been widespread concern.This paper analyzes the current development of robot and path planning algorithm and focuses on the advantages and disadvantages of the traditional intelligent path planning as well as the path planning.The problem of mobile robot path planning is studied by using ant colony algorithm, and it also provides some solving methods.

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.

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.

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

Cooperative path planning method is one of the research hotspots in the field of multi-agent. In this paper, we study the problem of cooperative path planning, in which the robots under the constraints of communication connectivity and collision avoidance, and choose the destination location independently. It is difficult to find the optimal solution efficiently in sparse solution space by large-scale complex environments. To overcome the problem, in this paper, a novel collaborative learning algorithm, named Park Algorithm (PA) is proposed, which makes full use of exploration experience to improve the exploration efficiency. First, an objective function with three goals including path length, communication and collision is designed. Second, Marker Matrix is designed to evaluate the exploration value of the grids in the map, and each robot uses its own and teammates' historical experience to update Marker Matrix. Our method can make full use of the exploration experience, realize self-learning and mutual learning between the robots, accelerate the exploration of solution space and avoid falling into local optimal solutions. Finally, the effectiveness and superiority of the proposed method are verified by simulation analysis. The results show that our method has a better performance compared with Genetic Algorithm (GA), Particle Swarm Optimization Algorithm (PSO) and Ant Colony Algorithm (ACO).

A novel seam extraction and path planning method for robotic welding of medium-thickness plate structural parts based on 3D vision

Robots are widely used in industry due to their efficiency and high accuracy in performance. One of the most intriguing issues in manufacturing stage of production line is to minimize significantly high percentage of energy consumed by these robot manipulators. The energy optimal control of robotic manipulators is a complex problem, as it requires a deep understanding of the robot's kinematics and dynamic behaviors. This paper propose a novel method of energy efficient path planning of an industrial robot arm in a workspace with multiple obstacles using differential evolution (DE) algorithm. The path-planning problem is formulated as an optimization problem with an aim to determine the shortest and energy optimal path of the robot arm from its given initial position to the predefined goal location, without hitting obstacles. Application of such evolutionary algorithms in trajectory planning is advantageous because the exact solution to the path-planning problem is not always available beforehand and must be determined dynamically. Experiments undertaken reveal that the DE-based path-planning algorithm outperforms its contenders in a significant manner.

Unmanned Aerial Vehicles (UAVs) are used in numerous military and civil application areas, and they have gained prominence in the research community. A UAV has to operate in a complex environment and checks the control points in the mission area by satisfying different constraints of the assigned task. Therefore, the path planning problem is one of the important areas in UAV researches. If the number of control points increases in the Unmanned Aerial System (UAS), finding a feasible solution in this large search space takes up a great deal of time. Nowadays low-cost UAVs are available, and this enables the use of multi-UAV systems to perform different tasks more efficiently and quickly. This usage increases the complexity of effective path planning and task allocation problem. This paper presents a flyable path planning for multi-UAV systems by using a Genetic Algorithm (GA) in a known environment at a constant altitude. A feasible path is firstly calculated by GAs, and then this path is smoothed by using Bezier curves. Experimental results indicate that the proposed approach produces effective and feasible paths for each UAV in a multi-UAV system. System is implemented in Java with a GUI for presenting results. The paper also draws future works that can be done on this topic.

Path planning is a core problem of the mobile robot navigation technology. However, few path plan methods that have been proposed consider the path smoothness and the robot attitude in the path. In this paper, a novel path planning method is proposed which considers three optimizing targets, including the path length, the path smoothness and the distance between the path and the obstacles. Firstly, a localization method which the path planning method relies on is explained. Secondly, a path planning model considering the path smoothness and the robot posture is discussed in detail. Thirdly, a solution based on particle swarm optimization (PSO) algorithm is discussed. Fourthly, simulation results are shown and analyzed. At last, conclusion and future work are provided.

In the task of path planning, the unmanned surface vessels (USV) are required to reach the destination while avoiding obstacle. However, it is difficult for USV to prioritize the two sub-target tasks of destination arrival and obstacle avoidance in a complex dynamic environment using traditional path planning methods. Based on the above considerations, a path planning method for USVs based on strategy integration is proposed. Firstly, the path planning scenario containing dynamic obstacles and two sub-target scenarios are set up. In order to strengthen its ability to reach the destination and avoid dynamic obstacles, the dynamic obstacles and the destination are removed separately in the two sub-target scenarios. Furthermore, the model for strategy selection is trained on action decisions in sub-target scenarios through double deep Q-Network with prioritized experience replay. Finally, a framework for strategy integration is designed to optimize strategy selection for USV. The simulation results show that the proposed method achieves a good overall performance and a higher path planning success rate than the traditional path planning method.

Background: Path planning technology has a wide range of applications in many fields. Applications in the field of high technology include: autonomous and touchless action of robots; obstacle avoidance and defense flight of drones; cruise missiles to avoid radar search, anti- bouncing attacks, and to complete the task of sudden defense and demolition. Applications in daily life include: GPS navigation; road planning based on GIS system; urban road network planning and navigation. Applications in the field of decision-making management include: vehicle problem (VRP) in logistics management and similar resource allocation problems in resource management. Routing problems in the field of communication technology. Any planning problem that can be topologized as a point and line network can basically be solved by the method of path planning. Different intelligent algorithms have different characteristics, and their application scope and fields are also different, so it is of great significance for the development of path planning technology to study the path planning intelligent algorithms from the characteristics of the algorithms themselves and their applications. Objective: Analyze the advantages and disadvantages of various types of planning algorithms, look forward to the future development trend of mobile robot path planning, and provide certain ideas for the research of mobile robot path planning. Methods: Search journals, patents, conferences, and papers related to mobile robot path planning, and summarize and analyze the advantages and disadvantages of various planning algorithms. Results: Based on the research results of many scholars, this study summarizes different mobile robot path planning methods. It is divided into four categories: traditional planning, intelligent search, artificial intelligence and local obstacle avoidance. This paper introduces and analyzes the latest research results of these types, including their design ideas, advantages and disadvantages, and improvement measures. The research methods adopted are analyzed in order to maximize the advantages of each algorithm and expand the application field of robot path planning to provide ideas and references. Conclusion: This paper provides guidance for the design and optimization of robot path planning. Finally, this paper summarizes the future development trend of robot path planning, and looks forward to the future development trend and key areas of robot path planning.

In this paper, path planning and sorting of packages for Omnidirectional-Wheel conveyor are presented using Reinforcement Learning (RL). Q-learning, Double Q-learning, Deep Q-learning, and the Double Deep Q-learning algorithms are investigated. The RL algorithms enable the conveyor to self-learn the packages path and sort them without using conventional control or path planning theories. The RL algorithms are used for two different case studies on conveyors structures with different numbers of cells to compare and evaluate their performances in large- and small-scale sized structures. To explore the proposed methods response to external environment effects, two types of collisions between multiple packages were considered, the proposed RL algorithms showed their ability to resolve both types successfully. Comparative study between multiple RL algorithms for path planning showed that the Q-learning and Double Q-learning algorithms had outperformed their Deep learning versions for path planning in the two case studies. Furthermore, the proposed RL methods are compared experimentally to classic control and path planning theories using a hardware prototype for one of the presented case studies. The hardware experimental results showed that the proposed RL methods were as successful as the conventional methods in path planning and sorting in much less processing time. Two types of sorting scenarios (Type I and II) were tested for same package type and for multiple ones. For Type I sorting the Q-learning algorithm performed better than the Q-learning with weights approach, achieving better mean and minimum rewards while maximum rewards remain the same for both techniques. As for Type II sorting, only the Q-learning with weights approach was able to achieve it and converge in a reasonable time.

This paper addresses the task assignment and path planning problem of multiple UAVs. As one of the strategies for a SEAD(Suppressions of Enemy Air Defense) mission, an efficiency strategy for assignment and path planning of homogeneous UAVs is developed. There are many path planning methods (e.g. potential function method or probabilistic roadmap method) to avoid obstacles, but the Voronoi diagram is used for the path planning with avoiding the “No fly zones” in this study. Since several candidate-paths are needed to solve the problem of timing constraints, the flyable paths in the shape of net is generated by using Voronoi diagram and multiple paths are found as the candidate paths from each UAV to each target. The objective function is determined as accumulated sum of the required time to finish the tasks on each target and the optimization is achieved by using genetic algorithm with appropriate representation of chromosome and genetic operators.

Robotics is a current emerging technology in the field of science. A number of universities in world are working in this field. Robotics is the new emerging booming field, which will be of great use to society in the coming years. These days many types of wireless robots are being developed and are put to varied applications and uses.This paper concerns the design and fabrication of the Automated Guided Robot (AGR) prototype, as a mainframe in helping the robot to generate a self-understanding of the area of work and mobilization to a destination desired by the user. The main objective of this paper is to create and develop a Path Planning Mobile Robot able to avoid obstacles in its path and reach a target designated position from its starting point utilizing 4 wheel-based rover body, sensors, linear motors and microcontrollers. The paper describes the design process of the AGR development, from the initiation of idea to development of algorithms, to virtual prototype development of AGR mechanisms and controller board, to fabrication, to mobility, obstacle avoidance and path planning test. Two methods of path planning are presented, using compass bearing in undetermined space and predetermined path. Obstacle avoidance and path planning algorithms has been tested using the completed design of AGR body.