轨迹优化matlab源代码并行停车的轨迹规划 微小情况下的并行停车运动计划方法％========================================= ====================================％时空分解的MATLAB源代码：一项知识并行停车运动优化的基于％的初始化策略”。 ％================================================== ============================ ％（C）2016 Bai Li版权所有。使用这些源代码产生新贡献时，使用者必须引用以下文章。 ％Bai Li等人，“时空分解：基于知识的初始化，用于并行停车运动优化的策略”，基于知识的系统，2016年。

多种算法实现路径规划，可以进行相互间的比较

目录 HCIP-Datacom-WAN Planning and Deployment V1.0 培训材料 HCIP-Datacom-WAN Planning and Deployment V1.0 版本说明.docx HCIP-Datacom-WAN Planning and Deployment V1.0 实验室搭建指南.docx；实验手册

网盘文件永久链接 01 园区网络与解决方案概述 02 园区网络架构与典型技术应用（下） 03 网络准入控制 03 网络准入控制实验演示 04 业务随行 05 VXLAN与园区网络虚拟化（上） 05 VXLAN与园区网络虚拟化（下） 06 园区多分支互联技术......

网盘文件永久链接 目录： 1.企业网络广域互联概述 2.GRE技术 3.QoS基本原理 4.HA技术 5.多业务网关介绍 6.SD-WAN解决方案技术概述 7.SD-WAN组网原理与规划1 8.SD-WAN组网原理与规划2 9.SD-WAN应用体验.......

这是一篇时空地图的论文

Gis urban planning and management design document城市规划管理gis方案设计文档

该项目的目的是开发和测试新颖的路径规划算法，该算法使用云和边缘计算资源的容量进行控制，满足时间和安全性约束。 该实验旨在揭示对本地和远程计算之间权衡的见解，并将导致新的决策机制接近最优，快速且节能。

％================================================== ============================％％“存在％移动障碍物的AGV快速轨迹规划的源代码：一种组合3维A *搜索和QCQP”。 ％李白，张友民，刘毅，钟祥，岑航杰，彭小燕，Kong启第33届中国控制与决策会议（CCDC），于2021年2月15日接受。％========= ================================================== ==================％（C）2021白李。 用户必须引用提到的相关文章％许可GNU通用公共许可v3.0％应该向IPOPT请求许可的AMPL。 将二进制文件放入％current文件夹中。 ％==================================================

This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning, planning under uncertainty, sensor-based planning, visibility, decision-theoretic planning, game theory, information spaces, reinforcement learning, nonlinear systems, trajectory planning, nonholonomic planning, and kinodynamic planning., Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning, but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the ‘configuration spaces’ of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. This text and reference is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.