[Motion Planning Algorithm Project Actual Combat] column introduction

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foreword

This blog column will introduce a series of robot motion planning algorithm projects and how to conduct experimental verification in the Gazebo simulation environment. We will provide runnable code and detailed algorithm analysis to help readers better understand and apply these algorithms.

Through the study of this column, readers will have a deep understanding of the principle and implementation of robot motion planning algorithms, as well as how to verify and debug in a simulation environment. Students and researchers interested in robotic motion planning algorithms, as well as engineers and developers looking to apply these algorithms in industrial and service robotics, will benefit from this column.

1. Interpolation methods commonly used in path planning

[Motion Planning Algorithm Project Combat] Interpolation methods commonly used in path planning (with ROS C++ code)
for common interpolation algorithms used to process path smoothing mainly include linear interpolation, cubic spline interpolation, B-spline interpolation and Bezier curve interpolation etc., respectively introduce, advantages and disadvantages, and usage scenarios.

[Motion Planning Algorithm] Cubic Spline Curve

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2. Commonly used thinning algorithms in path planning

[Motion Planning Algorithm Project Combat] The commonly used thinning algorithm in path planning (with ROS C++ code)
Simplification algorithm refers to streamlining the data and reducing the amount of data while keeping the data as undistorted as possible , to facilitate data storage and processing. In path planning, the function of the thinning algorithm is to remove redundant points in the original path, thereby reducing the number of path points, reducing the amount of calculation and storage, while retaining the shape characteristics and trajectory information of the path to ensure the accuracy of path planning and real-time. Common thinning algorithms include the Douglas-Peucker algorithm and the vertical distance limit method. This article will continue to introduce the principles of the two algorithms and provide code implementation.

3. How to load the path information of the csv file

[Motion planning algorithm project actual combat] How to load the path information of the csv file (with ROS C++ code)
In the motion planning algorithm project, path planning is a very important part. In practical applications, we usually need to load the pre-planned path into the program in some way for subsequent processing and motion control. As a common data exchange format, CSV files are also widely used to store path information. Therefore, it would be very helpful to learn how to load path information in CSV files. In ROS, we can use various programming languages ​​such as C++ and Python to load CSV files. Among them, C++, as the main development language of ROS, has good performance and stability, and is more suitable for use in practical applications. This article will take ROS C++ as an example to introduce how to load the path information in the CSV file.

4. How to implement A* algorithm in raster map

[Motion planning algorithm project actual combat] How to implement the A* algorithm in the grid map (with ROS C++ code)
In the path planning of the robot, the grid map is often used to describe the spatial layout of the environment. The A* algorithm is a search algorithm based on a raster map for finding the shortest path between two points.

[Motion Planning Algorithm] A* Algorithm

5. How to implement Dijkstra algorithm in raster map

[Motion Planning Algorithm Project Combat] How to implement Dijkstra's algorithm in a grid map (with ROS C++ code)
Dijkstra's algorithm is a greedy algorithm for the shortest path of a single source in a graph. Widely used in computer science and network design. The algorithm starts from the starting point and gradually expands the shortest path tree by preferentially selecting the unmarked nodes closest to the starting point. It ultimately determines the shortest paths to all nodes.

[Motion Planning Algorithm] Dijkstra Algorithm

6. How to implement a simple state machine

[Motion planning algorithm project combat] How to implement a simple state machine (with ROS C++ code) A
state machine is a formal model that represents the state transition of a robot, which consists of a set of states and a set of transition functions. In robotics, state can represent the state of the robot, such as motion state, perception state, control state, etc. The transition function represents the transition of the robot between different states, which can be manually defined or automatically generated according to rules.

7. How to realize robot multi-target navigation

[Motion planning algorithm project actual combat] How to realize robot multi-target point navigation (with ROS C++ code)
In ROS robot applications, it is a very common requirement to realize robot multi-target point navigation. This article will introduce how to use ROS and actionlib to realize the multi-target point navigation of the robot, and the target point information will be recorded in the YAML file. We can realize the robot's navigation function by using MoveBaseAction. MoveBaseAction is an action type in ROS, which provides an API to control the robot to reach the target point. It can receive goal points issued by ROS nodes and provide proper control for the robot to reach the goal point. In this article, we will use the actionlib library to implement the client side of MoveBaseAction.

8. Voronoi diagram

[Motion Planning Algorithm Project Combat] Voronoi diagram
Voronoi diagram, also known as Dirichlet diagram, is an important concept in computational geometry and graphics computing. It is a method of partitioning space into regions based on a set of points, where each region contains a nearest point. This concept can be applied in many fields such as robot path planning, computer vision, map making and many more. This article will introduce the concept, nature and application of Voronoi diagram, and discuss how to realize Voronoi diagram.

[stage simulation] multi-machine collaboration

9. Octree map

[Motion planning algorithm project combat] Octree map (with ROS C++ code)
Octomap (Octomap) is an open source software that uses octree data structure to represent three-dimensional space objects and environmental information, which can efficiently process and Store sensor data and generate high-resolution 3D maps. In the field of robotics, Octomap is widely used in ROS for tasks such as perception, path planning, and navigation. This article will introduce the basic principles, advantages and disadvantages, application scenarios and usage methods of Octomap to help readers better understand and apply this technology.

[Building Maps] Octree Mapping

10. How to implement cubic spline interpolation

[Motion planning algorithm project combat] How to implement cubic spline interpolation (with ROS C++ code)
Cubic spline interpolation is a method widely used in data fitting and interpolation. It interpolates between a given set of data points using a cubic polynomial to achieve a smooth fit. The advantage of cubic spline interpolation is that it can fit the given data points smoothly without oscillation or oscillation. Cubic spline interpolation is an interpolation method commonly used in robot path planning. In robot motion planning, a path is usually composed of multiple waypoints. The connections between waypoints require a smooth trajectory so that the robot can move smoothly. Cubic spline interpolation can be used to generate continuous trajectories between waypoints. This article will introduce the principle, advantages and disadvantages, application scenarios and implementation of ROS code of cubic spline interpolation, hoping to help readers better understand and apply this method.

[Motion Planning Algorithm] Cubic Spline Curve

11. How to use AABB for collision detection

【运动规划算法项目实战】如何利用AABB作碰撞检测（附ROS C++代码）
AABB碰撞检测是一种基于包围盒的简单、快速和有效的碰撞检测方法，被广泛应用于各种计算机图形学和物理仿真系统中。其基本思想是将物体或场景中的复杂几何形状简化为简单的矩形包围盒，然后判断两个包围盒之间是否存在重叠，以确定它们是否碰撞。AABB碰撞检测之所以受到广泛关注，是因为它既可以快速检测复杂物体之间的碰撞，又可以减少计算量和内存占用，适用于实时应用和大规模场景。本篇文章旨在帮助读者了解AABB碰撞检测的基础知识、应用场景、碰撞检测流程以及如何实现和优化AABB碰撞检测算法。

12. 如何使用分离轴定理算法实现碰撞检测

【运动规划算法项目实战】如何使用分离轴定理算法实现碰撞检测（附ROS C++代码）
分离轴定理（Separating Axis Theorem，简称SAT）是一种常用的碰撞检测算法，可以用于检测任何形状的物体之间的碰撞。在本文中，我们将介绍如何使用分离轴定理算法实现碰撞检测，并探讨其在自动驾驶和机器人领域中的应用。

13. 如何实现Dubins曲线和Reeds-Shepp曲线

【运动规划算法项目实战】如何实现Dubins曲线和Reeds-Shepp曲线（附ROS C++代码）
Dubins曲线和Reeds-Shepp曲线在机器人、自动驾驶行业中是非常重要的路径规划算法，它们能够有效地在不同的场景中生成最短路径，并且保证路径的可行性和安全性。本文将详细介绍Dubins曲线和Reeds-Shepp曲线的基本概念、算法实现、以及在机器人和自动驾驶领域的应用。

14. 如何使用Apollo Math模块实现碰撞检测

【运动规划算法项目实战】如何使用Apollo Math模块实现碰撞检测（附ROS C++代码）
在自动驾驶和机器人算法领域，碰撞检测是一项至关重要的任务。为了确保车辆或机器人能够安全地避免与障碍物发生碰撞，开发者需要使用高效准确的碰撞检测方法。Apollo开源平台的Math模块提供了强大的数学函数和工具，为碰撞检测提供了便捷的解决方案。

15. 如何生成运动物体的预测轨迹

【运动规划算法项目实战】如何生成运动物体的预测轨迹（附ROS C++代码）
自动驾驶、机器人行业中，准确预测运动物体的轨迹是实现高级感知、路径规划和决策的关键技术之一。通过预测其他车辆、行人或障碍物的未来运动，自动驾驶系统能够做出更准确的决策并规划出更安全高效的路径。在机器人领域，预测运动物体的轨迹对于协作操作、避障和人机交互等任务同样至关重要。本文将探讨运动物体预测轨迹生成的方法和技术，介绍其中一种开源项目Fast-Tracker，并展示如何使用Turtlebot3平台实现预测轨迹的生成。我们将重点关注预测轨迹的原理和实现，以及该技术在自动驾驶和机器人行业中的应用前景。

16. Fast-Tracker代码分析

【运动规划算法项目实战】Fast-Tracker代码分析
Fast-Tracker是浙江大学空中机器人实验室提出的一种系统化的解决方案，它可以无人飞行器 (UAV) 在混乱复杂的环境中自主安全地跟踪目标。

17. 如何使用Pure Pursuit算法进行路径跟踪

如何使用Pure Pursuit算法进行路径跟踪（附ROS C++代码）
在自动驾驶和机器人导航领域，路径跟踪是一项关键技术，它使车辆或机器人能够按照预定的路径规划进行移动。路径跟踪算法的选择对于系统性能和安全性至关重要。本文将介绍一种常用且有效的路径跟踪算法——Pure Pursuit算法。我们将详细讨论Pure Pursuit算法的原理、实现步骤以及如何在自动驾驶和机器人系统中应用该算法进行路径跟踪。

【运动规划算法】三次样条曲线跟踪

18. 如何使用MPC算法进行路径跟踪

【运动规划算法项目实战】如何使用MPC算法进行路径跟踪（附ROS C++代码）
自动驾驶和机器人领域中，路径跟踪是一项关键技术，它使车辆或机器人能够沿着预定轨迹行驶或移动。传统的控制方法往往难以应对复杂的动态环境和非线性特性，而模型预测控制（Model Predictive Control，简称MPC）作为一种先进的控制方法，具有良好的适应性和鲁棒性。本文将介绍MPC算法的原理和流程，并提供代码实现，帮助读者理解和应用MPC技术进行路径跟踪。

【运动控制算法】模型预测控制（1）

【运动控制算法】模型预测控制（2）

19. 基于梯度的轨迹优化算法GTOP

【运动规划算法项目实战】基于梯度的轨迹优化算法GTOP（附ROS C++代码）
在机器人轨迹规划领域，寻找一条安全、平滑且动力学可行的轨迹是一项重要的任务。GTOP（Gradient-Based Trajectory Optimizer）是一种基于梯度的在线轨迹优化框架，它通过最小化碰撞代价、平滑性和动力学可行性的惩罚来解决路径规划问题。本文将介绍如何使用GTOP进行轨迹优化，帮助读者了解该算法的原理和使用方法。

【运动规划算法】基于梯度的安全轨迹生成

20. 基于采样的路径搜索算法

【运动规划算法项目实战】基于采样的路径搜索算法（附ROS C++代码）
路径规划是机器人和自动驾驶等领域中的核心问题之一。通过选择合适的路径，机器人能够安全高效地从起点到达目标点。其中，基于采样的路径规划算法是一种常用且有效的方法。本文将重点介绍Probabilistic Roadmap（PRM）和Rapidly-exploring Random Tree（RRT）两种基于采样的路径规划算法。我们将深入探讨它们的原理、优缺点、代码实现，以帮助你了解它们在路径规划中的应用和优势。

【运动规划算法】PRM算法

【运动规划算法】RRT算法

21. 如何构建欧几里德符号距离场

【运动规划算法项目实战】如何构建欧几里德符号距离场（附ROS C++代码）
欧几里德符号距离场（Euclidean Signed Distance Field，ESDF）是机器人感知和路径规划中的关键数据结构之一。它能够提供环境中每个点到最近障碍物的距离，并且能够表示距离的方向（内部或外部）。ESDF在自动驾驶、机器人导航以及避障等应用中具有重要的作用。本文将介绍ESDF的构建过程和关键步骤，帮助读者深入了解ESDF的原理与实现。

22. Dynamic Window Approach算法

在机器人行业中，路径规划是一个关键的任务，它使得机器人能够在复杂的环境中自主导航。动态窗口方法（Dynamic Window Approach，DWA）算法是一种常用的路径规划算法之一，它允许机器人实时地根据环境和自身动力学限制进行路径的生成和评估。本文将详细介绍DWA算法的原理、在机器人路径规划中的应用、算法优缺点以及代码的实现。

【运动规划算法】DWA算法

23. Artificial Potential Field算法

In the robotics industry, path planning is a critical task that involves how to make robots move safely and efficiently in complex environments. Artificial Potential Field Algorithm (Artificial Potential Field Algorithm) is a commonly used path planning method, which draws on the concept of potential energy in physics, and guides the movement of the robot by simulating the action of force. This paper will introduce the principle of artificial potential field algorithm and its application in path planning in detail.

【Motion Planning Algorithm】Artificial Potential Field Algorithm

24. Time-Elastic-Band algorithm

[Motion Planning Algorithm] Time Elastic Band