Forest Fire UAV Path Planning Using Lawn Mower Search Strategy in MATLAB

在本项目中，我探索了在物理机器人上实现强化学习（RL）算法的过程，具体是在定制的3D打印机器人Benny和Bunny上从A到B的路径规划。作为我本科最后一年自选选修课的一部分，项目学习强化学习的基础知识。最初，编码直接在物理机器人上进行，但随着项目进展，意识到需要将算法与硬件解耦。仿真测试表明，在较小的状态空间（<= 100个状态）中表现良好，但在扩展到包含400个状态时，任何探索的RL算法均无法收敛。结果显示，在实现硬件前，需在仿真中探索更强大的算法。所有模拟代码均使用C++编写，确保代码的可移植性，以适应微控制器的限制，避免数据传输带来的复杂性。

CSDN佛怒唐莲上传的视频均有对应的完整代码，可直接运行，亲测可用，适合初学者使用。 代码压缩包内容： 主函数：main.m 调用函数：其他m文件 无需额外运行运行结果效果图 代码运行版本：Matlab 2019b；若运行出现错误，请根据提示修改。如不懂，欢迎私信博主。 运行操作步骤： 步骤一：将所有文件放置在Matlab的当前文件夹中。 步骤二：双击打开main.m文件。 步骤三：点击运行，待程序执行完毕后即可看到结果。 仿真咨询：如需其他服务，请私信博主或扫描视频中的QQ名片，提供以下服务： 完整代码提供 期刊或参考文献复现 Matlab程序定制 科研合作

《RRT_Star算法在三维与二维路径规划中的应用》RRT（Rapidly-exploring Random Trees）算法是一种用于复杂环境中寻找机器人路径的有效方法，属于概率道路规划的一种。其核心思想是通过随机生成树节点并逐步扩展树来探索配置空间，找到从起点到目标点的可行路径。在此基础上，RRT*（RRT Star）进一步优化，确保路径逐渐收敛到最优解。 本压缩包“RRT_Star_Algorithm.zip”包含RRT算法在三维和二维环境下的实现，提供了在MATLAB平台上的源代码，用户可根据需求进行修改。MATLAB因其强大的可视化功能*，非常适合进行路径规划仿真。 2D环境中的RRT*算法 二维环境中的RRT算法处理平面上的路径规划问题，例如无人机在二维空间中的飞行路径。算法通过在起点周围随机生成节点，选择离树最近的节点进行扩展，直线连接新节点并迭代直至找到目标点。2D文件夹*下代码展示了如何构建和优化搜索树。 3D环境中的RRT*算法 三维路径规划则适用于机器人在立体空间中的移动路径，如仓库机器人。三维空间中，路径不仅考虑x、y方向，还需处理z轴高度变化。3D文件夹中的代码展示了如何扩展RRT*算法处理三维空间路径规划，包括如何生成随机点、选择最近邻节点及更新树结构以逼近最优解。 RRT算法的优势在于其能有效处理高维配置空间，并在动态环境中适应性强，随着迭代，路径逐渐优化趋近最优解。用户可以通过阅读license.txt*文件了解使用许可协议，并对代码进行调整以适应不同的路径规划需求。

使用Dijkstra算法，寻求由起始点s到其他各点的最短路径树及其最短距离。

Golden Section Search Algorithm Overview of the Algorithm The Golden Section Search algorithm is an optimization technique used to find the extremum (maximum or minimum) of a unimodal function within a specified interval. It leverages the golden ratio to reduce the search interval step-by-step, ensuring efficient convergence. Steps of the Algorithm Initialize two points within the interval [a, b] using the golden ratio. Evaluate the function at these two points. Compare the function values and update the interval by removing the unnecessary part. Repeat the process until the desired precision is reached. Return the optimal point and function value. MATLAB Implementation Below is a sample MATLAB code to implement the Golden Section Search algorithm: function [x_opt, f_opt] = golden_section_search(f, a, b, tol) phi = (1 + sqrt(5)) / 2; c = b - (b - a) / phi; d = a + (b - a) / phi; while abs(b - a) > tol if f(c) < f xss=removed xss=removed xss=removed xss=removed xss=removed xss=removed> This code defines a function golden_section_search that finds the optimal point within the interval [a, b] using Golden Section Search. Advantages Efficient for unimodal functions. Simple to implement with minimal function evaluations. Converges faster than other search methods for specific cases.

Simultaneous Heat Transfer Search (SHTS) is a single-objective optimization technique designed for unconstrained problems. Unlike traditional heat transfer search, which utilizes only one heat transfer mode per iteration, SHTS divides the population and simultaneously applies all three heat transfer modes. The main differences between SHTS and HTS can be found in the following references: Synchronous Heat Transfer Search for Costly Numerical Optimization, 2016 IEEE Congress on Evolutionary Computation (CEC), 2016, pp. 2982-2988; IEEE Paper Link Simultaneous Heat Transfer Search for Single-Objective Real Parameter Optimization Problems, TENCON 2016 IEEE, pp. 2138-2141, 2016; IEEE Paper Link Note: If N is the population size, exactly N function evaluations are required during a complete iteration of SHTS. For T iterations, the total number of function evaluations will be NT.

This is a Harris corner detection program written in MATLAB. You can give it a try to detect keypoints in images based on the Harris corner detection method.

本书提供了用Matlab进行数值计算的丰富资料，内容可读性、知识性和实用性都非常强。

In this article, we will use MATLAB to simulate the ECC algorithm, exploring each step of the simulation process. ECC (Elliptic Curve Cryptography) is a widely-used cryptographic algorithm known for its efficiency and security. Through MATLAB, you can effectively simulate ECC to understand its key operations and performance. Below are the detailed steps for implementation: Step 1: Setup MATLAB Environment To begin, ensure you have MATLAB installed and configured with necessary libraries. Load any required ECC-related toolboxes or files. Step 2: Define ECC Parameters Define the parameters for the elliptic curve such as prime modulus, base point, and curve equation. These are crucial in generating secure keys and verifying the cryptographic functionality. Step 3: Implement Key Generation Using ECC, you can create public and private keys. In MATLAB, code the key generation process by selecting random integers for the private key and calculating the public key based on ECC operations. Step 4: Encryption and Decryption Simulation Simulate the encryption process where a plaintext message is converted into an ECC point and then encrypted with the public key. For decryption, utilize the private key to retrieve the original message. Step 5: Verify Algorithm Performance Analyze the computational performance of ECC in MATLAB, focusing on encryption speed, memory usage, and any points of optimization. This helps in understanding ECC's advantages in cryptographic applications. By following these steps, you'll have a robust ECC simulation in MATLAB, providing insights into the algorithm's implementation and potential optimizations.