IOP Conference Series: Earth and Environmental Science PAPER • OPEN ACCESS An overview of path planning algorithms To cite this article: Zhuozhen Tang and Hongzhong Ma 2021 IOP Conf. Ser.: Earth Environ. Sci. 804 022024 View the article online for updates and enhancements. This content was downloaded from IP address 170.106.35.76 on 23/09/2021 at 23:53 6th International Conference on Energy Science and Applied Technology IOP Publishing IOP Conf. Series: Earth and Environmental Science 804 (2021) 022024 doi:10.1088/1755-1315/804/2/022024 An overview of path planning algorithms Zhuozhen Tang *, Hongzhong Ma Hohai university, Nanjing 211100, Jiangsu, China *Corresponding author Email: [email protected] Abstract. This paper reviews the basic concepts of path planning, classifies environmental modeling methods, analyzes the significance of V2X environment modeling, and summarizes the existing path planning algorithms. Different algorithm can be adjusted in time according to different environments to improve the efficiency of path planning. In addition, according to the advantages and disadvantages of different algorithms, each algorithm is fused, which can effectively avoid the shortcomings of each algorithm and improve the efficiency of the planning algorithm. Keywords: environmental modelling; V2X environmental; path planning algorithm. 1. Introduction The path planning part belongs to the control or decision-making part of the unmanned vehicle and vessel architecture system (Fig. 1), and it is also a key subject to be solved urgently in the unmanned vehicle technology. The performance of path planning module is directly related to the advantages and disadvantages of vehicle driving path selection and driving fluency. With the wide application of intelligent vehicles, ships and mobile robots in different fields, more effective path planning algorithms have been proposed by researchers. environment sensors V2X actuators Perceptions Planning Control Environment Target Action Model Figure 1. Vehicle and vessel architecture system 2. Basic concepts of path planning At present, the concept of path planning that is relatively recognized in the academic field is as follows: According to different degrees of understanding of environmental information, there are two kinds of global path planning, that environmental information is fully known and local path planning where Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Published under licence by IOP Publishing Ltd 1 6th International Conference on Energy Science and Applied Technology IOP Publishing IOP Conf. Series: Earth and Environmental Science 804 (2021) 022024 doi:10.1088/1755-1315/804/2/022024 environmental information is partially unknown or completely unknown. According to whether the obstacle is moving in the environment, it can be divided into static planning with static environment unchanged and dynamic planning with obstacle movement. According to whether the number of controllable variables in the mobile robot system is less than the dimension of its attitude space, it can be divided into the motion planning of the non-holonomic system, the motion planning of the holonomic system, and the path planning of the kinematics and dynamics system considering both kinematics and dynamics constraints. [6] Global path planning, also known as global navigation planning. Pure geometric path planning from the starting point to the target point, independent of time series and vehicle dynamics; Local path planning is also called obstacle avoidance planning, dynamic path planning, or real-time navigation planning. Mainly to detect obstacles, and the Moving trajectory Tracking of obstacles (Moving Object Detection and Tracking, usually abbreviated MODAT) to make the next step may position, finally draw a picture contains collision risk existing and potential collision risk map of obstacles, the potential risk warning level is 100 milliseconds, the future need to be further improved, the sensor, the algorithm of computing power efficiency and the processor is a great challenge, consider not only the obstacle avoidance planning space is also considering time series, in downtown complex computation. It can be more than 30TFLOPS, which is the most difficult part of the smart car. 3. Environment Modelling To carry out path planning, first, a moving environment model of the mobile machine should be established. With the environment model (different from the understanding of the environment), path planning is constituted by the path search. Environment representations can be divided into static and dynamic environments. The static environment is relatively simple, but in practical application, it is more dynamic environment that changes in real time. In the complex unknown environment, the ground unmanned vehicle usually lacks the global environmental information, but the V2X environmental model can provide rich environmental information, which also makes the vehicle trajectory obtained by the initial value and constraints more. This also puts forward high real-time and robust requirements for path planning algorithm in complex environment. Therefore, it is of great significance to study the path planning algorithm of flexible strain in complex environment. At present, the existing environmental modeling methods are mainly responsible for collecting the road and lane information around the self-driving vehicle through the road map subsystem, and expressing it in the map with geometric and topological characteristics, including interconnection and restriction. The main content of road mapping subsystem is map representation and map creation. The simplest way to create a road map is to extract a manual annotation of the road shape from an aerial image. For dynamic environment, MOT subsystem (also known as Multi-Target Detection and Tracking -DATMO) is mainly used to detect and track the posture of moving obstacles in the environment around the autonomous vehicle. 3.1. Traditional environmental modeling methods The traditional MOT method mainly includes three steps: data segmentation, data association and filtering. In the data segmentation stage, clustering or pattern recognition technology is used to segment the sensor data. In the data association step, data association techniques are used to associate data segments with targets (moving obstacles). In the filtering phase, for each target, the position is estimated by taking the geometric mean of the data assigned to the target. The position estimate is usually updated by Kalman filter or particle filter. 3.2. Model-based approach Model-based approaches infer directly from sensor data, using a physical model of the sensor and a geometric model of the object, and using non-parametric filters (such as particle filters). The data 2 6th International Conference on Energy Science and Applied Technology IOP Publishing IOP Conf. Series: Earth and Environmental Science 804 (2021) 022024 doi:10.1088/1755-1315/804/2/022024 segmentation and association steps are not required because the geometric object model associates the data to the target. 3.3. Based on stereo vision method The method relies on the color and depth information provided by stereo image pair to detect and track the moving obstacles in the environment. Ess et al. proposed an obstacle detection and identification method using only synchronous video from a forward-looking stereo camera. Their work focuses on obstacle tracking based on the output of pedestrian and vehicle detectors per frame. For obstacle detection, they used a support vector machine (SVM) classifier with directional gradient histogram (HOG) feature to classify each image area as obstacle or non-obstacle. 3.4. Based on raster map method Based on the method of raster map, the occupancy raster map of dynamic environment is first constructed. The map-building step is followed by data segmentation, data association, and filtering steps to provide an object-level representation of the scene. Nguyen et al. proposed a grid-based method for detecting and tracking moving objects in stereo cameras. Their work focuses on pedestrian detection and tracking. Reconstruction of 3D points from stereo image pairs. Using the inverse sensor model, the occupancy probability of each cell in the grid map is estimated based on the related three-dimensional points. 3.5. Based on sensor fusion method Sensor fusion based approaches fuse data from various sensors, such as lidar, radar, and cameras, to explore their respective characteristics and improve environmental awareness. The MOT subsystem is divided into two layers. The sensor layer extracts feature from the sensor data that can be used to describe the moving obstacle hypothesis according to the point model or box model. The sensor layer also attempts to associate features with current prediction assumptions from the fusion layer. Functions that cannot be associated with existing assumptions are used to generate new recommendations. Generate observations for each feature associated with a given hypothesis, encapsulating all the information needed to update the hypothesis state estimate. The fusion layer selects the best tracking model for each hypothesis based on the recommendations and observations provided by the sensor layer, and uses the Kalman filter to estimate (or update) the estimate of the hypothesis